├── .gitattributes
├── .gitignore
├── Appendix A - A Crash Course in Python.ipynb
├── Chapter 1 - Installing Python, SAS SWAT, and CAS.ipynb
├── Chapter 10 - Advanced Topics.ipynb
├── Chapter 2 - The 10 Minute Guide to Using CAS from Python.ipynb
├── Chapter 3 - The Fundamentals of using Python with CAS.ipynb
├── Chapter 4 - Managing Your Data in CAS.ipynb
├── Chapter 5 - The CASAction and CASTable Objects.ipynb
├── Chapter 6 - Working with CAS Tables.ipynb
├── Chapter 7 - Data Exploration and Summary Statistics.ipynb
├── Chapter 8 - Modeling Continuous Variables.ipynb
├── Chapter 9 - Modeling Categorical Variables.ipynb
├── LICENSE
├── README.md
├── cover.jpg
└── data
    ├── README.md
    └── organics.csv.zip


/.gitattributes:
--------------------------------------------------------------------------------
 1 | * text=auto
 2 | # enforce text on certain files
 3 | *.py text
 4 | *.pyx text
 5 | *.pyd text
 6 | *.c text
 7 | *.h text
 8 | *.html text
 9 | *.csv text
10 | *.json text
11 | *.pickle binary
12 | *.h5 binary
13 | *.dta binary
14 | *.xls binary
15 | *.xlsx binary
16 | *.bat text eol=crlf
17 | *.html text
18 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
 1 | ## Editor temporary/working/backup files
 2 | .#*
 3 | *\#*\#
 4 | [#]*#
 5 | *~
 6 | *$
 7 | *.bak
 8 | *.old
 9 | *flymake*
10 | *.kdev4
11 | *.log
12 | *.swo
13 | *.swp
14 | *.pdb
15 | .project
16 | .pydevproject
17 | .settings
18 | .idea
19 | .vagrant
20 | .noseids
21 | .ipynb_checkpoints
22 | .tags
23 | 
24 | ## Compiled source
25 | *.a
26 | *.com
27 | *.class
28 | *.dll
29 | *.dylib
30 | *.exe
31 | *.o
32 | *.py[ocd]
33 | *.so
34 | .build_cache_dir
35 | MANIFEST
36 | 
37 | ## Python files
38 | # setup.py working directory
39 | build
40 | # sphinx build directory
41 | doc/_build
42 | # setup.py dist directory
43 | dist
44 | # Egg metadata
45 | *.egg-info
46 | .eggs
47 | .pypirc
48 | 
49 | ## tox testing tool
50 | .tox
51 | # rope
52 | .ropeproject
53 | # wheel files
54 | *.whl
55 | **/wheelhouse/*
56 | # coverage
57 | .coverage
58 | swat.egg-info/
59 | __pycache__/
60 | _stats.txt
61 | cover/
62 | test-reports/
63 | 
64 | ## OS generated files
65 | .directory
66 | .gdb_history
67 | .DS_Store
68 | ehthumbs.db
69 | Icon?
70 | Thumbs.db
71 | 
72 | ## Documentation generated files
73 | doc/build
74 | doc/source/generated
75 | 


--------------------------------------------------------------------------------
/Chapter 1 - Installing Python, SAS SWAT, and CAS.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "# Chapter 1 - Installing Python, SAS SWAT, and CAS\n",
 8 |     "\n",
 9 |     "After installing Python (we recommend \n",
10 |     "[the Anaconda distribution from Continuum.io](https://www.continuum.io/downloads)),\n",
11 |     "you can get the most recent version of SAS SWAT (Scripting Wrapper for Analytics Transfer)\n",
12 |     "from GitHub at https://github.com/sassoftware/python-swat/releases.  Simply run \n",
13 |     "`pip install <URL>` on the appropriate installer for your platform, or use the source\n",
14 |     "code tar.gz if your platform isn't supported.  In the latter case, you will need to\n",
15 |     "use the REST interface of CAS.  \n",
16 |     "\n",
17 |     "Once you have that installed and you have a CAS\n",
18 |     "server available, you can make your first connection."
19 |    ]
20 |   },
21 |   {
22 |    "cell_type": "markdown",
23 |    "metadata": {},
24 |    "source": [
25 |     "## Making Your First Connection"
26 |    ]
27 |   },
28 |   {
29 |    "cell_type": "code",
30 |    "execution_count": null,
31 |    "metadata": {
32 |     "collapsed": true
33 |    },
34 |    "outputs": [],
35 |    "source": [
36 |     "import swat\n",
37 |     "\n",
38 |     "conn = swat.CAS('server-name.mycompany.com', port-number, 'userid', 'password')"
39 |    ]
40 |   },
41 |   {
42 |    "cell_type": "code",
43 |    "execution_count": null,
44 |    "metadata": {
45 |     "collapsed": true
46 |    },
47 |    "outputs": [],
48 |    "source": [
49 |     "conn.serverstatus()"
50 |    ]
51 |   },
52 |   {
53 |    "cell_type": "code",
54 |    "execution_count": null,
55 |    "metadata": {
56 |     "collapsed": true
57 |    },
58 |    "outputs": [],
59 |    "source": [
60 |     "conn.close()"
61 |    ]
62 |   }
63 |  ],
64 |  "metadata": {
65 |   "kernelspec": {
66 |    "display_name": "Python 3",
67 |    "language": "python",
68 |    "name": "python3"
69 |   },
70 |   "language_info": {
71 |    "codemirror_mode": {
72 |     "name": "ipython",
73 |     "version": 3
74 |    },
75 |    "file_extension": ".py",
76 |    "mimetype": "text/x-python",
77 |    "name": "python",
78 |    "nbconvert_exporter": "python",
79 |    "pygments_lexer": "ipython3",
80 |    "version": "3.4.5"
81 |   }
82 |  },
83 |  "nbformat": 4,
84 |  "nbformat_minor": 0
85 | }
86 | 


--------------------------------------------------------------------------------
/Chapter 10 - Advanced Topics.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Chapter 10 - Advanced Topics"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 1,
 13 |    "metadata": {
 14 |     "collapsed": true
 15 |    },
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import swat"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 2,
 24 |    "metadata": {
 25 |     "collapsed": true
 26 |    },
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "conn = swat.CAS('server-name.mycompany.com', 5570, 'username', 'password')"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "markdown",
 34 |    "metadata": {},
 35 |    "source": [
 36 |     "## Binary vs REST Interfaces"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "markdown",
 41 |    "metadata": {},
 42 |    "source": [
 43 |     "### The Binary Interface"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "code",
 48 |    "execution_count": null,
 49 |    "metadata": {
 50 |     "collapsed": true
 51 |    },
 52 |    "outputs": [],
 53 |    "source": [
 54 |     "binconn = swat.CAS('server-name.mycompany.com', 5570, protocol='cas')"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "markdown",
 59 |    "metadata": {},
 60 |    "source": [
 61 |     "### The REST Interface"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": null,
 67 |    "metadata": {
 68 |     "collapsed": true
 69 |    },
 70 |    "outputs": [],
 71 |    "source": [
 72 |     "restconn = swat.CAS('server-name.mycompany.com', 8888, protocol='http')"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "markdown",
 77 |    "metadata": {},
 78 |    "source": [
 79 |     "## Result Processing Workflows"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "markdown",
 84 |    "metadata": {},
 85 |    "source": [
 86 |     "### The Easy Way"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 3,
 92 |    "metadata": {
 93 |     "collapsed": false
 94 |    },
 95 |    "outputs": [
 96 |     {
 97 |      "name": "stdout",
 98 |      "output_type": "stream",
 99 |      "text": [
100 |       "NOTE: Cloud Analytic Services made the file data/iris.csv available as table DATA.IRIS in caslib CASUSER(username).\n"
101 |      ]
102 |     }
103 |    ],
104 |    "source": [
105 |     "tbl = conn.loadtable('data/iris.csv', caslib='casuser').casTable"
106 |    ]
107 |   },
108 |   {
109 |    "cell_type": "code",
110 |    "execution_count": 4,
111 |    "metadata": {
112 |     "collapsed": false
113 |    },
114 |    "outputs": [
115 |     {
116 |      "data": {
117 |       "text/html": [
118 |        "<div class=\"cas-results-key\"><b>&#167; Summary</b></div>\n",
119 |        "<div class=\"cas-results-body\">\n",
120 |        "<div>\n",
121 |        "<table border=\"1\" class=\"dataframe\"><caption>Descriptive Statistics for DATA.IRIS</caption>\n",
122 |        "  <thead>\n",
123 |        "    <tr style=\"text-align: right;\">\n",
124 |        "      <th title=\"\"></th>\n",
125 |        "      <th title=\"Analysis Variable\">Column</th>\n",
126 |        "      <th title=\"Minimum\">Min</th>\n",
127 |        "      <th title=\"Maximum\">Max</th>\n",
128 |        "      <th title=\"N\">N</th>\n",
129 |        "      <th title=\"N Miss\">NMiss</th>\n",
130 |        "      <th title=\"Mean\">Mean</th>\n",
131 |        "      <th title=\"Sum\">Sum</th>\n",
132 |        "      <th title=\"Std Dev\">Std</th>\n",
133 |        "      <th title=\"Std Error\">StdErr</th>\n",
134 |        "      <th title=\"Variance\">Var</th>\n",
135 |        "      <th title=\"USS\">USS</th>\n",
136 |        "      <th title=\"Corrected SS\">CSS</th>\n",
137 |        "      <th title=\"Coeff of Variation\">CV</th>\n",
138 |        "      <th title=\"t Value\">TValue</th>\n",
139 |        "      <th title=\"Pr &gt; |t|\">ProbT</th>\n",
140 |        "    </tr>\n",
141 |        "  </thead>\n",
142 |        "  <tbody>\n",
143 |        "    <tr>\n",
144 |        "      <th>0</th>\n",
145 |        "      <td>sepal_length</td>\n",
146 |        "      <td>4.3</td>\n",
147 |        "      <td>7.9</td>\n",
148 |        "      <td>150.0</td>\n",
149 |        "      <td>0.0</td>\n",
150 |        "      <td>5.843333</td>\n",
151 |        "      <td>876.5</td>\n",
152 |        "      <td>0.828066</td>\n",
153 |        "      <td>0.067611</td>\n",
154 |        "      <td>0.685694</td>\n",
155 |        "      <td>5223.85</td>\n",
156 |        "      <td>102.168333</td>\n",
157 |        "      <td>14.171126</td>\n",
158 |        "      <td>86.425375</td>\n",
159 |        "      <td>3.331256e-129</td>\n",
160 |        "    </tr>\n",
161 |        "    <tr>\n",
162 |        "      <th>1</th>\n",
163 |        "      <td>sepal_width</td>\n",
164 |        "      <td>2.0</td>\n",
165 |        "      <td>4.4</td>\n",
166 |        "      <td>150.0</td>\n",
167 |        "      <td>0.0</td>\n",
168 |        "      <td>3.054000</td>\n",
169 |        "      <td>458.1</td>\n",
170 |        "      <td>0.433594</td>\n",
171 |        "      <td>0.035403</td>\n",
172 |        "      <td>0.188004</td>\n",
173 |        "      <td>1427.05</td>\n",
174 |        "      <td>28.012600</td>\n",
175 |        "      <td>14.197587</td>\n",
176 |        "      <td>86.264297</td>\n",
177 |        "      <td>4.374977e-129</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <th>2</th>\n",
181 |        "      <td>petal_length</td>\n",
182 |        "      <td>1.0</td>\n",
183 |        "      <td>6.9</td>\n",
184 |        "      <td>150.0</td>\n",
185 |        "      <td>0.0</td>\n",
186 |        "      <td>3.758667</td>\n",
187 |        "      <td>563.8</td>\n",
188 |        "      <td>1.764420</td>\n",
189 |        "      <td>0.144064</td>\n",
190 |        "      <td>3.113179</td>\n",
191 |        "      <td>2583.00</td>\n",
192 |        "      <td>463.863733</td>\n",
193 |        "      <td>46.942721</td>\n",
194 |        "      <td>26.090198</td>\n",
195 |        "      <td>1.994305e-57</td>\n",
196 |        "    </tr>\n",
197 |        "    <tr>\n",
198 |        "      <th>3</th>\n",
199 |        "      <td>petal_width</td>\n",
200 |        "      <td>0.1</td>\n",
201 |        "      <td>2.5</td>\n",
202 |        "      <td>150.0</td>\n",
203 |        "      <td>0.0</td>\n",
204 |        "      <td>1.198667</td>\n",
205 |        "      <td>179.8</td>\n",
206 |        "      <td>0.763161</td>\n",
207 |        "      <td>0.062312</td>\n",
208 |        "      <td>0.582414</td>\n",
209 |        "      <td>302.30</td>\n",
210 |        "      <td>86.779733</td>\n",
211 |        "      <td>63.667470</td>\n",
212 |        "      <td>19.236588</td>\n",
213 |        "      <td>3.209704e-42</td>\n",
214 |        "    </tr>\n",
215 |        "  </tbody>\n",
216 |        "</table>\n",
217 |        "</div>\n",
218 |        "</div>\n",
219 |        "<div class=\"cas-output-area\"></div>\n",
220 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.025s</span> &#183; <span class=\"cas-user\">user 0.027s</span> &#183; <span class=\"cas-sys\">sys 0.002s</span> &#183; <span class=\"cas-memory\">mem 1.74MB</span></small></p>"
221 |       ],
222 |       "text/plain": [
223 |        "[Summary]\n",
224 |        "\n",
225 |        " Descriptive Statistics for DATA.IRIS\n",
226 |        " \n",
227 |        "          Column  Min  Max      N  NMiss      Mean    Sum       Std    StdErr  \\\n",
228 |        " 0  sepal_length  4.3  7.9  150.0    0.0  5.843333  876.5  0.828066  0.067611   \n",
229 |        " 1   sepal_width  2.0  4.4  150.0    0.0  3.054000  458.1  0.433594  0.035403   \n",
230 |        " 2  petal_length  1.0  6.9  150.0    0.0  3.758667  563.8  1.764420  0.144064   \n",
231 |        " 3   petal_width  0.1  2.5  150.0    0.0  1.198667  179.8  0.763161  0.062312   \n",
232 |        " \n",
233 |        "         Var      USS         CSS         CV     TValue          ProbT  \n",
234 |        " 0  0.685694  5223.85  102.168333  14.171126  86.425375  3.331256e-129  \n",
235 |        " 1  0.188004  1427.05   28.012600  14.197587  86.264297  4.374977e-129  \n",
236 |        " 2  3.113179  2583.00  463.863733  46.942721  26.090198   1.994305e-57  \n",
237 |        " 3  0.582414   302.30   86.779733  63.667470  19.236588   3.209704e-42  \n",
238 |        "\n",
239 |        "+ Elapsed: 0.025s, user: 0.027s, sys: 0.002s, mem: 1.74mb"
240 |       ]
241 |      },
242 |      "execution_count": 4,
243 |      "metadata": {},
244 |      "output_type": "execute_result"
245 |     }
246 |    ],
247 |    "source": [
248 |     "out = tbl.summary()\n",
249 |     "out"
250 |    ]
251 |   },
252 |   {
253 |    "cell_type": "markdown",
254 |    "metadata": {},
255 |    "source": [
256 |     "### Using Response and Result Callbacks"
257 |    ]
258 |   },
259 |   {
260 |    "cell_type": "code",
261 |    "execution_count": 5,
262 |    "metadata": {
263 |     "collapsed": true
264 |    },
265 |    "outputs": [],
266 |    "source": [
267 |     "def result_cb(key, value, response, connection, userdata):\n",
268 |     "    print('\\n>>> RESULT %s\\n' % key)\n",
269 |     "    print(value)\n",
270 |     "    return userdata"
271 |    ]
272 |   },
273 |   {
274 |    "cell_type": "code",
275 |    "execution_count": 7,
276 |    "metadata": {
277 |     "collapsed": false
278 |    },
279 |    "outputs": [
280 |     {
281 |      "name": "stdout",
282 |      "output_type": "stream",
283 |      "text": [
284 |       "\n",
285 |       ">>> RESULT ByGroupInfo\n",
286 |       "\n",
287 |       "ByGroupInfo\n",
288 |       "\n",
289 |       "      species   species_f       _key_\n",
290 |       "0      setosa      setosa      setosa\n",
291 |       "1  versicolor  versicolor  versicolor\n",
292 |       "2   virginica   virginica   virginica\n",
293 |       "\n",
294 |       ">>> RESULT ByGroup1.Summary\n",
295 |       "\n",
296 |       "Descriptive Statistics for DATA.IRIS\n",
297 |       "\n",
298 |       "               Column  Min  Max\n",
299 |       "species                        \n",
300 |       "setosa   sepal_length  4.3  5.8\n",
301 |       "setosa    sepal_width  2.3  4.4\n",
302 |       "setosa   petal_length  1.0  1.9\n",
303 |       "setosa    petal_width  0.1  0.6\n",
304 |       "\n",
305 |       ">>> RESULT ByGroup2.Summary\n",
306 |       "\n",
307 |       "Descriptive Statistics for DATA.IRIS\n",
308 |       "\n",
309 |       "                  Column  Min  Max\n",
310 |       "species                           \n",
311 |       "versicolor  sepal_length  4.9  7.0\n",
312 |       "versicolor   sepal_width  2.0  3.4\n",
313 |       "versicolor  petal_length  3.0  5.1\n",
314 |       "versicolor   petal_width  1.0  1.8\n",
315 |       "\n",
316 |       ">>> RESULT ByGroup3.Summary\n",
317 |       "\n",
318 |       "Descriptive Statistics for DATA.IRIS\n",
319 |       "\n",
320 |       "                 Column  Min  Max\n",
321 |       "species                          \n",
322 |       "virginica  sepal_length  4.9  7.9\n",
323 |       "virginica   sepal_width  2.2  3.8\n",
324 |       "virginica  petal_length  4.5  6.9\n",
325 |       "virginica   petal_width  1.4  2.5\n"
326 |      ]
327 |     }
328 |    ],
329 |    "source": [
330 |     "tbl.groupby('species').summary(resultfunc=result_cb, subset=['min', 'max'])"
331 |    ]
332 |   },
333 |   {
334 |    "cell_type": "code",
335 |    "execution_count": 8,
336 |    "metadata": {
337 |     "collapsed": true
338 |    },
339 |    "outputs": [],
340 |    "source": [
341 |     "def response_cb(response, connection, userdata):\n",
342 |     "    print('\\n>>> RESPONSE')\n",
343 |     "    for k, v in response:\n",
344 |     "        print('\\n>>> RESULT %s\\n' % k)\n",
345 |     "        print(v)\n",
346 |     "    return userdata"
347 |    ]
348 |   },
349 |   {
350 |    "cell_type": "code",
351 |    "execution_count": 9,
352 |    "metadata": {
353 |     "collapsed": false
354 |    },
355 |    "outputs": [
356 |     {
357 |      "name": "stdout",
358 |      "output_type": "stream",
359 |      "text": [
360 |       "\n",
361 |       ">>> RESPONSE\n",
362 |       "\n",
363 |       ">>> RESULT ByGroupInfo\n",
364 |       "\n",
365 |       "ByGroupInfo\n",
366 |       "\n",
367 |       "      species   species_f       _key_\n",
368 |       "0      setosa      setosa      setosa\n",
369 |       "1  versicolor  versicolor  versicolor\n",
370 |       "2   virginica   virginica   virginica\n",
371 |       "\n",
372 |       ">>> RESPONSE\n",
373 |       "\n",
374 |       ">>> RESULT ByGroup1.Summary\n",
375 |       "\n",
376 |       "Descriptive Statistics for DATA.IRIS\n",
377 |       "\n",
378 |       "               Column  Min  Max\n",
379 |       "species                        \n",
380 |       "setosa   sepal_length  4.3  5.8\n",
381 |       "setosa    sepal_width  2.3  4.4\n",
382 |       "setosa   petal_length  1.0  1.9\n",
383 |       "setosa    petal_width  0.1  0.6\n",
384 |       "\n",
385 |       ">>> RESPONSE\n",
386 |       "\n",
387 |       ">>> RESULT ByGroup2.Summary\n",
388 |       "\n",
389 |       "Descriptive Statistics for DATA.IRIS\n",
390 |       "\n",
391 |       "                  Column  Min  Max\n",
392 |       "species                           \n",
393 |       "versicolor  sepal_length  4.9  7.0\n",
394 |       "versicolor   sepal_width  2.0  3.4\n",
395 |       "versicolor  petal_length  3.0  5.1\n",
396 |       "versicolor   petal_width  1.0  1.8\n",
397 |       "\n",
398 |       ">>> RESPONSE\n",
399 |       "\n",
400 |       ">>> RESULT ByGroup3.Summary\n",
401 |       "\n",
402 |       "Descriptive Statistics for DATA.IRIS\n",
403 |       "\n",
404 |       "                 Column  Min  Max\n",
405 |       "species                          \n",
406 |       "virginica  sepal_length  4.9  7.9\n",
407 |       "virginica   sepal_width  2.2  3.8\n",
408 |       "virginica  petal_length  4.5  6.9\n",
409 |       "virginica   petal_width  1.4  2.5\n",
410 |       "\n",
411 |       ">>> RESPONSE\n"
412 |      ]
413 |     }
414 |    ],
415 |    "source": [
416 |     "tbl.groupby('species').summary(responsefunc=response_cb, subset=['min', 'max'])"
417 |    ]
418 |   },
419 |   {
420 |    "cell_type": "markdown",
421 |    "metadata": {},
422 |    "source": [
423 |     "### Handling Responses from Multiple Sessions Simultaneously"
424 |    ]
425 |   },
426 |   {
427 |    "cell_type": "code",
428 |    "execution_count": 18,
429 |    "metadata": {
430 |     "collapsed": true
431 |    },
432 |    "outputs": [],
433 |    "source": [
434 |     "conn1 = swat.CAS()\n",
435 |     "conn2 = swat.CAS()"
436 |    ]
437 |   },
438 |   {
439 |    "cell_type": "code",
440 |    "execution_count": 20,
441 |    "metadata": {
442 |     "collapsed": false
443 |    },
444 |    "outputs": [
445 |     {
446 |      "name": "stdout",
447 |      "output_type": "stream",
448 |      "text": [
449 |       "NOTE: Cloud Analytic Services made the file data/iris.csv available as table DATA.IRIS in caslib CASUSER(username).\n",
450 |       "NOTE: Cloud Analytic Services made the file data/iris.csv available as table DATA.IRIS in caslib CASUSER(username).\n"
451 |      ]
452 |     }
453 |    ],
454 |    "source": [
455 |     "tbl1 = conn1.loadtable('data/iris.csv', caslib='casuser').casTable\n",
456 |     "tbl2 = conn2.loadtable('data/iris.csv', caslib='casuser').casTable"
457 |    ]
458 |   },
459 |   {
460 |    "cell_type": "code",
461 |    "execution_count": 27,
462 |    "metadata": {
463 |     "collapsed": false
464 |    },
465 |    "outputs": [],
466 |    "source": [
467 |     "conn1 = tbl1.groupby('species').invoke('summary', subset=['min', 'max'])"
468 |    ]
469 |   },
470 |   {
471 |    "cell_type": "code",
472 |    "execution_count": 28,
473 |    "metadata": {
474 |     "collapsed": false
475 |    },
476 |    "outputs": [
477 |     {
478 |      "name": "stdout",
479 |      "output_type": "stream",
480 |      "text": [
481 |       "\n",
482 |       ">>> RESPONSE\n",
483 |       "\n",
484 |       ">>> RESULT ByGroupInfo\n",
485 |       "\n",
486 |       "ByGroupInfo\n",
487 |       "\n",
488 |       "      species   species_f       _key_\n",
489 |       "0      setosa      setosa      setosa\n",
490 |       "1  versicolor  versicolor  versicolor\n",
491 |       "2   virginica   virginica   virginica\n",
492 |       "\n",
493 |       ">>> RESPONSE\n",
494 |       "\n",
495 |       ">>> RESULT ByGroup1.Summary\n",
496 |       "\n",
497 |       "Descriptive Statistics for DATA.IRIS\n",
498 |       "\n",
499 |       "               Column  Min  Max\n",
500 |       "species                        \n",
501 |       "setosa   sepal_length  4.3  5.8\n",
502 |       "setosa    sepal_width  2.3  4.4\n",
503 |       "setosa   petal_length  1.0  1.9\n",
504 |       "setosa    petal_width  0.1  0.6\n",
505 |       "\n",
506 |       ">>> RESPONSE\n",
507 |       "\n",
508 |       ">>> RESULT ByGroup2.Summary\n",
509 |       "\n",
510 |       "Descriptive Statistics for DATA.IRIS\n",
511 |       "\n",
512 |       "                  Column  Min  Max\n",
513 |       "species                           \n",
514 |       "versicolor  sepal_length  4.9  7.0\n",
515 |       "versicolor   sepal_width  2.0  3.4\n",
516 |       "versicolor  petal_length  3.0  5.1\n",
517 |       "versicolor   petal_width  1.0  1.8\n",
518 |       "\n",
519 |       ">>> RESPONSE\n",
520 |       "\n",
521 |       ">>> RESULT ByGroup3.Summary\n",
522 |       "\n",
523 |       "Descriptive Statistics for DATA.IRIS\n",
524 |       "\n",
525 |       "                 Column  Min  Max\n",
526 |       "species                          \n",
527 |       "virginica  sepal_length  4.9  7.9\n",
528 |       "virginica   sepal_width  2.2  3.8\n",
529 |       "virginica  petal_length  4.5  6.9\n",
530 |       "virginica   petal_width  1.4  2.5\n",
531 |       "\n",
532 |       ">>> RESPONSE\n"
533 |      ]
534 |     }
535 |    ],
536 |    "source": [
537 |     "for resp in conn1:\n",
538 |     "    print('\\n>>> RESPONSE')\n",
539 |     "    for k, v in resp:\n",
540 |     "        print('\\n>>> RESULT %s\\n' % k)\n",
541 |     "        print(v)"
542 |    ]
543 |   },
544 |   {
545 |    "cell_type": "code",
546 |    "execution_count": 29,
547 |    "metadata": {
548 |     "collapsed": true
549 |    },
550 |    "outputs": [],
551 |    "source": [
552 |     "conn1 = tbl1.groupby('species').invoke('summary', subset=['min', 'max'])\n",
553 |     "conn2 = tbl2.groupby('species').invoke('topk', topk=1, bottomk=1)"
554 |    ]
555 |   },
556 |   {
557 |    "cell_type": "code",
558 |    "execution_count": 30,
559 |    "metadata": {
560 |     "collapsed": false
561 |    },
562 |    "outputs": [
563 |     {
564 |      "name": "stdout",
565 |      "output_type": "stream",
566 |      "text": [
567 |       "\n",
568 |       ">>> RESPONSE\n",
569 |       "\n",
570 |       ">>> RESULT ByGroupInfo\n",
571 |       "\n",
572 |       "ByGroupInfo\n",
573 |       "\n",
574 |       "      species   species_f       _key_\n",
575 |       "0      setosa      setosa      setosa\n",
576 |       "1  versicolor  versicolor  versicolor\n",
577 |       "2   virginica   virginica   virginica\n",
578 |       "\n",
579 |       ">>> RESPONSE\n",
580 |       "\n",
581 |       ">>> RESULT ByGroup1.Summary\n",
582 |       "\n",
583 |       "Descriptive Statistics for DATA.IRIS\n",
584 |       "\n",
585 |       "               Column  Min  Max\n",
586 |       "species                        \n",
587 |       "setosa   sepal_length  4.3  5.8\n",
588 |       "setosa    sepal_width  2.3  4.4\n",
589 |       "setosa   petal_length  1.0  1.9\n",
590 |       "setosa    petal_width  0.1  0.6\n",
591 |       "\n",
592 |       ">>> RESPONSE\n",
593 |       "\n",
594 |       ">>> RESULT ByGroup2.Summary\n",
595 |       "\n",
596 |       "Descriptive Statistics for DATA.IRIS\n",
597 |       "\n",
598 |       "                  Column  Min  Max\n",
599 |       "species                           \n",
600 |       "versicolor  sepal_length  4.9  7.0\n",
601 |       "versicolor   sepal_width  2.0  3.4\n",
602 |       "versicolor  petal_length  3.0  5.1\n",
603 |       "versicolor   petal_width  1.0  1.8\n",
604 |       "\n",
605 |       ">>> RESPONSE\n",
606 |       "\n",
607 |       ">>> RESULT ByGroup3.Summary\n",
608 |       "\n",
609 |       "Descriptive Statistics for DATA.IRIS\n",
610 |       "\n",
611 |       "                 Column  Min  Max\n",
612 |       "species                          \n",
613 |       "virginica  sepal_length  4.9  7.9\n",
614 |       "virginica   sepal_width  2.2  3.8\n",
615 |       "virginica  petal_length  4.5  6.9\n",
616 |       "virginica   petal_width  1.4  2.5\n",
617 |       "\n",
618 |       ">>> RESPONSE\n",
619 |       "\n",
620 |       ">>> RESPONSE\n",
621 |       "\n",
622 |       ">>> RESULT ByGroupInfo\n",
623 |       "\n",
624 |       "ByGroupInfo\n",
625 |       "\n",
626 |       "      species   species_f       _key_\n",
627 |       "0      setosa      setosa      setosa\n",
628 |       "1  versicolor  versicolor  versicolor\n",
629 |       "2   virginica   virginica   virginica\n",
630 |       "\n",
631 |       ">>> RESPONSE\n",
632 |       "\n",
633 |       ">>> RESULT ByGroup1.Topk\n",
634 |       "\n",
635 |       "Top and Bottom Distinct Values for DATA.IRIS\n",
636 |       "\n",
637 |       "               Column        FmtVar  Rank\n",
638 |       "species                                  \n",
639 |       "setosa   sepal_length           5.8     1\n",
640 |       "setosa   sepal_length             5    15\n",
641 |       "setosa    sepal_width           4.4     1\n",
642 |       "setosa    sepal_width             3    16\n",
643 |       "setosa   petal_length           1.9     1\n",
644 |       "setosa   petal_length             1     9\n",
645 |       "setosa    petal_width           0.6     1\n",
646 |       "setosa    petal_width           0.1     6\n",
647 |       "setosa        species        setosa     1\n",
648 |       "setosa        species        setosa     1\n",
649 |       "\n",
650 |       ">>> RESPONSE\n",
651 |       "\n",
652 |       ">>> RESULT ByGroup1.TopkMisc\n",
653 |       "\n",
654 |       "Miscellaneous Information on Distinct Values for DATA.IRIS\n",
655 |       "\n",
656 |       "               Column   N  TruncatedTopk  TruncatedBtmk  ScoreOther\n",
657 |       "species                                                            \n",
658 |       "setosa   sepal_length  15              0              0         NaN\n",
659 |       "setosa    sepal_width  16              0              0         NaN\n",
660 |       "setosa   petal_length   9              0              0         NaN\n",
661 |       "setosa    petal_width   6              0              0         NaN\n",
662 |       "setosa        species   1              0              0         NaN\n",
663 |       "\n",
664 |       ">>> RESPONSE\n",
665 |       "\n",
666 |       ">>> RESULT ByGroup2.Topk\n",
667 |       "\n",
668 |       "Top and Bottom Distinct Values for DATA.IRIS\n",
669 |       "\n",
670 |       "                  Column        FmtVar  Rank\n",
671 |       "species                                     \n",
672 |       "versicolor  sepal_length           6.9     1\n",
673 |       "versicolor  sepal_length             5    21\n",
674 |       "versicolor   sepal_width           3.4     1\n",
675 |       "versicolor   sepal_width             2    14\n",
676 |       "versicolor  petal_length           5.1     1\n",
677 |       "versicolor  petal_length             3    19\n",
678 |       "versicolor   petal_width           1.8     1\n",
679 |       "versicolor   petal_width             1     9\n",
680 |       "versicolor       species    versicolor     1\n",
681 |       "versicolor       species    versicolor     1\n",
682 |       "\n",
683 |       ">>> RESPONSE\n",
684 |       "\n",
685 |       ">>> RESULT ByGroup2.TopkMisc\n",
686 |       "\n",
687 |       "Miscellaneous Information on Distinct Values for DATA.IRIS\n",
688 |       "\n",
689 |       "                  Column   N  TruncatedTopk  TruncatedBtmk  ScoreOther\n",
690 |       "species                                                               \n",
691 |       "versicolor  sepal_length  21              0              0         NaN\n",
692 |       "versicolor   sepal_width  14              0              0         NaN\n",
693 |       "versicolor  petal_length  19              0              0         NaN\n",
694 |       "versicolor   petal_width   9              0              0         NaN\n",
695 |       "versicolor       species   1              0              0         NaN\n",
696 |       "\n",
697 |       ">>> RESPONSE\n",
698 |       "\n",
699 |       ">>> RESULT ByGroup3.Topk\n",
700 |       "\n",
701 |       "Top and Bottom Distinct Values for DATA.IRIS\n",
702 |       "\n",
703 |       "                 Column        FmtVar  Rank\n",
704 |       "species                                    \n",
705 |       "virginica  sepal_length           7.9     1\n",
706 |       "virginica  sepal_length             6    21\n",
707 |       "virginica   sepal_width           3.8     1\n",
708 |       "virginica   sepal_width             3    13\n",
709 |       "virginica  petal_length           6.9     1\n",
710 |       "virginica  petal_length             5    20\n",
711 |       "virginica   petal_width           2.5     1\n",
712 |       "virginica   petal_width             2    12\n",
713 |       "virginica       species     virginica     1\n",
714 |       "virginica       species     virginica     1\n",
715 |       "\n",
716 |       ">>> RESPONSE\n",
717 |       "\n",
718 |       ">>> RESULT ByGroup3.TopkMisc\n",
719 |       "\n",
720 |       "Miscellaneous Information on Distinct Values for DATA.IRIS\n",
721 |       "\n",
722 |       "                 Column   N  TruncatedTopk  TruncatedBtmk  ScoreOther\n",
723 |       "species                                                              \n",
724 |       "virginica  sepal_length  21              0              0         NaN\n",
725 |       "virginica   sepal_width  13              0              0         NaN\n",
726 |       "virginica  petal_length  20              0              0         NaN\n",
727 |       "virginica   petal_width  12              0              0         NaN\n",
728 |       "virginica       species   1              0              0         NaN\n",
729 |       "\n",
730 |       ">>> RESPONSE\n"
731 |      ]
732 |     }
733 |    ],
734 |    "source": [
735 |     "for resp, c in swat.getnext(conn1, conn2):\n",
736 |     "    print('\\n>>> RESPONSE')\n",
737 |     "    for k, v in resp:\n",
738 |     "        print('\\n>>> RESULT %s\\n' % k)\n",
739 |     "        print(v)"
740 |    ]
741 |   },
742 |   {
743 |    "cell_type": "markdown",
744 |    "metadata": {},
745 |    "source": [
746 |     "## Connecting to Existing Sessions"
747 |    ]
748 |   },
749 |   {
750 |    "cell_type": "code",
751 |    "execution_count": 31,
752 |    "metadata": {
753 |     "collapsed": false
754 |    },
755 |    "outputs": [
756 |     {
757 |      "data": {
758 |       "text/plain": [
759 |        "CAS('server-name.mycompany.com', 5570, 'username', protocol='cas', name='py-session-1', session='2fed25d6-4946-0540-8308-73c7f75b53c6')"
760 |       ]
761 |      },
762 |      "execution_count": 31,
763 |      "metadata": {},
764 |      "output_type": "execute_result"
765 |     }
766 |    ],
767 |    "source": [
768 |     "conn"
769 |    ]
770 |   },
771 |   {
772 |    "cell_type": "code",
773 |    "execution_count": 32,
774 |    "metadata": {
775 |     "collapsed": false
776 |    },
777 |    "outputs": [
778 |     {
779 |      "data": {
780 |       "text/html": [
781 |        "<div class=\"cas-results-key\"><b>&#167; Session</b></div>\n",
782 |        "<div class=\"cas-results-body\">\n",
783 |        "<div>\n",
784 |        "<table border=\"1\" class=\"dataframe\">\n",
785 |        "  <thead>\n",
786 |        "    <tr style=\"text-align: right;\">\n",
787 |        "      <th title=\"\"></th>\n",
788 |        "      <th title=\"Name\">SessionName</th>\n",
789 |        "      <th title=\"UUID\">UUID</th>\n",
790 |        "      <th title=\"Session Properties\">State</th>\n",
791 |        "      <th title=\"Authentication Type\">Authentication</th>\n",
792 |        "      <th title=\"Userid\">Userid</th>\n",
793 |        "    </tr>\n",
794 |        "  </thead>\n",
795 |        "  <tbody>\n",
796 |        "    <tr>\n",
797 |        "      <th>0</th>\n",
798 |        "      <td>py-session-3:Fri Nov  4 10:32:16 2016</td>\n",
799 |        "      <td>e7f753f7-4e20-aa46-ac6a-4c574cede3c5</td>\n",
800 |        "      <td>Connected</td>\n",
801 |        "      <td>Active Directory</td>\n",
802 |        "      <td>username</td>\n",
803 |        "    </tr>\n",
804 |        "    <tr>\n",
805 |        "      <th>1</th>\n",
806 |        "      <td>py-session-1:Fri Nov  4 11:46:25 2016</td>\n",
807 |        "      <td>2fed25d6-4946-0540-8308-73c7f75b53c6</td>\n",
808 |        "      <td>Connected</td>\n",
809 |        "      <td>Active Directory</td>\n",
810 |        "      <td>username</td>\n",
811 |        "    </tr>\n",
812 |        "    <tr>\n",
813 |        "      <th>2</th>\n",
814 |        "      <td>py-session-2:Fri Nov  4 12:06:29 2016</td>\n",
815 |        "      <td>6f04be18-1146-0642-8437-9e928edbf42d</td>\n",
816 |        "      <td>Connected</td>\n",
817 |        "      <td>Active Directory</td>\n",
818 |        "      <td>username</td>\n",
819 |        "    </tr>\n",
820 |        "    <tr>\n",
821 |        "      <th>3</th>\n",
822 |        "      <td>py-session-4:Fri Nov  4 12:08:52 2016</td>\n",
823 |        "      <td>21e426a9-20f8-ef4e-a606-cc699f8e274a</td>\n",
824 |        "      <td>Connected</td>\n",
825 |        "      <td>Active Directory</td>\n",
826 |        "      <td>username</td>\n",
827 |        "    </tr>\n",
828 |        "    <tr>\n",
829 |        "      <th>4</th>\n",
830 |        "      <td>py-session-5:Fri Nov  4 12:08:52 2016</td>\n",
831 |        "      <td>430d8336-2b9b-f54b-8163-86ebe476b238</td>\n",
832 |        "      <td>Connected</td>\n",
833 |        "      <td>Active Directory</td>\n",
834 |        "      <td>username</td>\n",
835 |        "    </tr>\n",
836 |        "  </tbody>\n",
837 |        "</table>\n",
838 |        "</div>\n",
839 |        "</div>\n",
840 |        "<div class=\"cas-output-area\"></div>\n",
841 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.000406s</span> &#183; <span class=\"cas-memory\">mem 0.0912MB</span></small></p>"
842 |       ],
843 |       "text/plain": [
844 |        "[Session]\n",
845 |        "\n",
846 |        "                              SessionName  \\\n",
847 |        " 0  py-session-3:Fri Nov  4 10:32:16 2016   \n",
848 |        " 1  py-session-1:Fri Nov  4 11:46:25 2016   \n",
849 |        " 2  py-session-2:Fri Nov  4 12:06:29 2016   \n",
850 |        " 3  py-session-4:Fri Nov  4 12:08:52 2016   \n",
851 |        " 4  py-session-5:Fri Nov  4 12:08:52 2016   \n",
852 |        " \n",
853 |        "                                    UUID      State    Authentication  Userid  \n",
854 |        " 0  e7f753f7-4e20-aa46-ac6a-4c574cede3c5  Connected  Active Directory  username  \n",
855 |        " 1  2fed25d6-4946-0540-8308-73c7f75b53c6  Connected  Active Directory  username  \n",
856 |        " 2  6f04be18-1146-0642-8437-9e928edbf42d  Connected  Active Directory  username  \n",
857 |        " 3  21e426a9-20f8-ef4e-a606-cc699f8e274a  Connected  Active Directory  username  \n",
858 |        " 4  430d8336-2b9b-f54b-8163-86ebe476b238  Connected  Active Directory  username  \n",
859 |        "\n",
860 |        "+ Elapsed: 0.000406s, mem: 0.0912mb"
861 |       ]
862 |      },
863 |      "execution_count": 32,
864 |      "metadata": {},
865 |      "output_type": "execute_result"
866 |     }
867 |    ],
868 |    "source": [
869 |     "conn.listsessions()"
870 |    ]
871 |   },
872 |   {
873 |    "cell_type": "code",
874 |    "execution_count": 33,
875 |    "metadata": {
876 |     "collapsed": true
877 |    },
878 |    "outputs": [],
879 |    "source": [
880 |     "conn2 = swat.CAS('server-name.mycompany.com', 5570, session='2fed25d6-4946-0540-8308-73c7f75b53c6')"
881 |    ]
882 |   },
883 |   {
884 |    "cell_type": "code",
885 |    "execution_count": 34,
886 |    "metadata": {
887 |     "collapsed": false
888 |    },
889 |    "outputs": [
890 |     {
891 |      "data": {
892 |       "text/plain": [
893 |        "CAS('server-name.mycompany.com', 5570, 'username', protocol='cas', name='py-session-6', session='2fed25d6-4946-0540-8308-73c7f75b53c6')"
894 |       ]
895 |      },
896 |      "execution_count": 34,
897 |      "metadata": {},
898 |      "output_type": "execute_result"
899 |     }
900 |    ],
901 |    "source": [
902 |     "conn2"
903 |    ]
904 |   },
905 |   {
906 |    "cell_type": "code",
907 |    "execution_count": null,
908 |    "metadata": {
909 |     "collapsed": true
910 |    },
911 |    "outputs": [],
912 |    "source": []
913 |   }
914 |  ],
915 |  "metadata": {
916 |   "kernelspec": {
917 |    "display_name": "Python 3",
918 |    "language": "python",
919 |    "name": "python3"
920 |   },
921 |   "language_info": {
922 |    "codemirror_mode": {
923 |     "name": "ipython",
924 |     "version": 3
925 |    },
926 |    "file_extension": ".py",
927 |    "mimetype": "text/x-python",
928 |    "name": "python",
929 |    "nbconvert_exporter": "python",
930 |    "pygments_lexer": "ipython3",
931 |    "version": "3.4.5"
932 |   }
933 |  },
934 |  "nbformat": 4,
935 |  "nbformat_minor": 0
936 | }
937 | 


--------------------------------------------------------------------------------
/Chapter 5 - The CASAction and CASTable Objects.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Chapter 5 - The CASAction and CASTable Objects"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "## Getting Started with CASAction Objects"
  15 |    ]
  16 |   },
  17 |   {
  18 |    "cell_type": "code",
  19 |    "execution_count": 1,
  20 |    "metadata": {
  21 |     "collapsed": true
  22 |    },
  23 |    "outputs": [],
  24 |    "source": [
  25 |     "import swat"
  26 |    ]
  27 |   },
  28 |   {
  29 |    "cell_type": "code",
  30 |    "execution_count": 2,
  31 |    "metadata": {
  32 |     "collapsed": true
  33 |    },
  34 |    "outputs": [],
  35 |    "source": [
  36 |     "conn = swat.CAS('server-name.mycompany.com', 5570, 'username', 'password')"
  37 |    ]
  38 |   },
  39 |   {
  40 |    "cell_type": "code",
  41 |    "execution_count": null,
  42 |    "metadata": {
  43 |     "collapsed": true
  44 |    },
  45 |    "outputs": [],
  46 |    "source": [
  47 |     "conn.fetch?"
  48 |    ]
  49 |   },
  50 |   {
  51 |    "cell_type": "markdown",
  52 |    "metadata": {},
  53 |    "source": [
  54 |     "Create an instance of the Fetch object."
  55 |    ]
  56 |   },
  57 |   {
  58 |    "cell_type": "code",
  59 |    "execution_count": 3,
  60 |    "metadata": {
  61 |     "collapsed": true
  62 |    },
  63 |    "outputs": [],
  64 |    "source": [
  65 |     "fa = conn.Fetch()"
  66 |    ]
  67 |   },
  68 |   {
  69 |    "cell_type": "code",
  70 |    "execution_count": 4,
  71 |    "metadata": {
  72 |     "collapsed": false
  73 |    },
  74 |    "outputs": [
  75 |     {
  76 |      "data": {
  77 |       "text/plain": [
  78 |        "swat.cas.actions.table.Fetch"
  79 |       ]
  80 |      },
  81 |      "execution_count": 4,
  82 |      "metadata": {},
  83 |      "output_type": "execute_result"
  84 |     }
  85 |    ],
  86 |    "source": [
  87 |     "type(fa)"
  88 |    ]
  89 |   },
  90 |   {
  91 |    "cell_type": "code",
  92 |    "execution_count": 5,
  93 |    "metadata": {
  94 |     "collapsed": false
  95 |    },
  96 |    "outputs": [
  97 |     {
  98 |      "data": {
  99 |       "text/plain": [
 100 |        "(swat.cas.actions.CASAction,)"
 101 |       ]
 102 |      },
 103 |      "execution_count": 5,
 104 |      "metadata": {},
 105 |      "output_type": "execute_result"
 106 |     }
 107 |    ],
 108 |    "source": [
 109 |     "type(fa).__bases__"
 110 |    ]
 111 |   },
 112 |   {
 113 |    "cell_type": "code",
 114 |    "execution_count": 6,
 115 |    "metadata": {
 116 |     "collapsed": false
 117 |    },
 118 |    "outputs": [
 119 |     {
 120 |      "name": "stdout",
 121 |      "output_type": "stream",
 122 |      "text": [
 123 |       "NOTE: Cloud Analytic Services made the file data/iris.csv available as table DATA.IRIS in caslib CASUSER(username).\n"
 124 |      ]
 125 |     }
 126 |    ],
 127 |    "source": [
 128 |     "out = conn.loadtable('data/iris.csv', caslib='casuser')"
 129 |    ]
 130 |   },
 131 |   {
 132 |    "cell_type": "markdown",
 133 |    "metadata": {},
 134 |    "source": [
 135 |     "Call the CASAction instance."
 136 |    ]
 137 |   },
 138 |   {
 139 |    "cell_type": "code",
 140 |    "execution_count": 7,
 141 |    "metadata": {
 142 |     "collapsed": false
 143 |    },
 144 |    "outputs": [
 145 |     {
 146 |      "data": {
 147 |       "text/html": [
 148 |        "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
 149 |        "<div class=\"cas-results-body\">\n",
 150 |        "<div>\n",
 151 |        "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table DATA.IRIS</caption>\n",
 152 |        "  <thead>\n",
 153 |        "    <tr style=\"text-align: right;\">\n",
 154 |        "      <th title=\"\"></th>\n",
 155 |        "      <th title=\"sepal_length\">sepal_length</th>\n",
 156 |        "      <th title=\"sepal_width\">sepal_width</th>\n",
 157 |        "      <th title=\"petal_length\">petal_length</th>\n",
 158 |        "      <th title=\"petal_width\">petal_width</th>\n",
 159 |        "      <th title=\"species\">species</th>\n",
 160 |        "    </tr>\n",
 161 |        "  </thead>\n",
 162 |        "  <tbody>\n",
 163 |        "    <tr>\n",
 164 |        "      <th>0</th>\n",
 165 |        "      <td>5.1</td>\n",
 166 |        "      <td>3.5</td>\n",
 167 |        "      <td>1.4</td>\n",
 168 |        "      <td>0.2</td>\n",
 169 |        "      <td>setosa</td>\n",
 170 |        "    </tr>\n",
 171 |        "    <tr>\n",
 172 |        "      <th>1</th>\n",
 173 |        "      <td>4.9</td>\n",
 174 |        "      <td>3.0</td>\n",
 175 |        "      <td>1.4</td>\n",
 176 |        "      <td>0.2</td>\n",
 177 |        "      <td>setosa</td>\n",
 178 |        "    </tr>\n",
 179 |        "    <tr>\n",
 180 |        "      <th>2</th>\n",
 181 |        "      <td>4.7</td>\n",
 182 |        "      <td>3.2</td>\n",
 183 |        "      <td>1.3</td>\n",
 184 |        "      <td>0.2</td>\n",
 185 |        "      <td>setosa</td>\n",
 186 |        "    </tr>\n",
 187 |        "    <tr>\n",
 188 |        "      <th>3</th>\n",
 189 |        "      <td>4.6</td>\n",
 190 |        "      <td>3.1</td>\n",
 191 |        "      <td>1.5</td>\n",
 192 |        "      <td>0.2</td>\n",
 193 |        "      <td>setosa</td>\n",
 194 |        "    </tr>\n",
 195 |        "    <tr>\n",
 196 |        "      <th>4</th>\n",
 197 |        "      <td>5.0</td>\n",
 198 |        "      <td>3.6</td>\n",
 199 |        "      <td>1.4</td>\n",
 200 |        "      <td>0.2</td>\n",
 201 |        "      <td>setosa</td>\n",
 202 |        "    </tr>\n",
 203 |        "  </tbody>\n",
 204 |        "</table>\n",
 205 |        "</div>\n",
 206 |        "</div>\n",
 207 |        "<div class=\"cas-output-area\"></div>\n",
 208 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00369s</span> &#183; <span class=\"cas-user\">user 0.002s</span> &#183; <span class=\"cas-sys\">sys 0.001s</span> &#183; <span class=\"cas-memory\">mem 1.65MB</span></small></p>"
 209 |       ],
 210 |       "text/plain": [
 211 |        "[Fetch]\n",
 212 |        "\n",
 213 |        " Selected Rows from Table DATA.IRIS\n",
 214 |        " \n",
 215 |        "    sepal_length  sepal_width  petal_length  petal_width species\n",
 216 |        " 0           5.1          3.5           1.4          0.2  setosa\n",
 217 |        " 1           4.9          3.0           1.4          0.2  setosa\n",
 218 |        " 2           4.7          3.2           1.3          0.2  setosa\n",
 219 |        " 3           4.6          3.1           1.5          0.2  setosa\n",
 220 |        " 4           5.0          3.6           1.4          0.2  setosa\n",
 221 |        "\n",
 222 |        "+ Elapsed: 0.00369s, user: 0.002s, sys: 0.001s, mem: 1.65mb"
 223 |       ]
 224 |      },
 225 |      "execution_count": 7,
 226 |      "metadata": {},
 227 |      "output_type": "execute_result"
 228 |     }
 229 |    ],
 230 |    "source": [
 231 |     "fa(table=dict(name='data.iris', caslib='casuser'), to=5)"
 232 |    ]
 233 |   },
 234 |   {
 235 |    "cell_type": "code",
 236 |    "execution_count": 8,
 237 |    "metadata": {
 238 |     "collapsed": false
 239 |    },
 240 |    "outputs": [
 241 |     {
 242 |      "data": {
 243 |       "text/html": [
 244 |        "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
 245 |        "<div class=\"cas-results-body\">\n",
 246 |        "<div>\n",
 247 |        "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table DATA.IRIS</caption>\n",
 248 |        "  <thead>\n",
 249 |        "    <tr style=\"text-align: right;\">\n",
 250 |        "      <th title=\"\"></th>\n",
 251 |        "      <th title=\"sepal_length\">sepal_length</th>\n",
 252 |        "      <th title=\"sepal_width\">sepal_width</th>\n",
 253 |        "      <th title=\"petal_length\">petal_length</th>\n",
 254 |        "      <th title=\"petal_width\">petal_width</th>\n",
 255 |        "      <th title=\"species\">species</th>\n",
 256 |        "    </tr>\n",
 257 |        "  </thead>\n",
 258 |        "  <tbody>\n",
 259 |        "    <tr>\n",
 260 |        "      <th>0</th>\n",
 261 |        "      <td>4.3</td>\n",
 262 |        "      <td>3.0</td>\n",
 263 |        "      <td>1.1</td>\n",
 264 |        "      <td>0.1</td>\n",
 265 |        "      <td>setosa</td>\n",
 266 |        "    </tr>\n",
 267 |        "    <tr>\n",
 268 |        "      <th>1</th>\n",
 269 |        "      <td>4.4</td>\n",
 270 |        "      <td>2.9</td>\n",
 271 |        "      <td>1.4</td>\n",
 272 |        "      <td>0.2</td>\n",
 273 |        "      <td>setosa</td>\n",
 274 |        "    </tr>\n",
 275 |        "    <tr>\n",
 276 |        "      <th>2</th>\n",
 277 |        "      <td>4.4</td>\n",
 278 |        "      <td>3.0</td>\n",
 279 |        "      <td>1.3</td>\n",
 280 |        "      <td>0.2</td>\n",
 281 |        "      <td>setosa</td>\n",
 282 |        "    </tr>\n",
 283 |        "    <tr>\n",
 284 |        "      <th>3</th>\n",
 285 |        "      <td>4.4</td>\n",
 286 |        "      <td>3.2</td>\n",
 287 |        "      <td>1.3</td>\n",
 288 |        "      <td>0.2</td>\n",
 289 |        "      <td>setosa</td>\n",
 290 |        "    </tr>\n",
 291 |        "    <tr>\n",
 292 |        "      <th>4</th>\n",
 293 |        "      <td>4.5</td>\n",
 294 |        "      <td>2.3</td>\n",
 295 |        "      <td>1.3</td>\n",
 296 |        "      <td>0.3</td>\n",
 297 |        "      <td>setosa</td>\n",
 298 |        "    </tr>\n",
 299 |        "  </tbody>\n",
 300 |        "</table>\n",
 301 |        "</div>\n",
 302 |        "</div>\n",
 303 |        "<div class=\"cas-output-area\"></div>\n",
 304 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0145s</span> &#183; <span class=\"cas-user\">user 0.008s</span> &#183; <span class=\"cas-sys\">sys 0.006s</span> &#183; <span class=\"cas-memory\">mem 8.58MB</span></small></p>"
 305 |       ],
 306 |       "text/plain": [
 307 |        "[Fetch]\n",
 308 |        "\n",
 309 |        " Selected Rows from Table DATA.IRIS\n",
 310 |        " \n",
 311 |        "    sepal_length  sepal_width  petal_length  petal_width species\n",
 312 |        " 0           4.3          3.0           1.1          0.1  setosa\n",
 313 |        " 1           4.4          2.9           1.4          0.2  setosa\n",
 314 |        " 2           4.4          3.0           1.3          0.2  setosa\n",
 315 |        " 3           4.4          3.2           1.3          0.2  setosa\n",
 316 |        " 4           4.5          2.3           1.3          0.3  setosa\n",
 317 |        "\n",
 318 |        "+ Elapsed: 0.0145s, user: 0.008s, sys: 0.006s, mem: 8.58mb"
 319 |       ]
 320 |      },
 321 |      "execution_count": 8,
 322 |      "metadata": {},
 323 |      "output_type": "execute_result"
 324 |     }
 325 |    ],
 326 |    "source": [
 327 |     "fa(table=dict(name='data.iris', caslib='casuser'), to=5,\n",
 328 |     "              sortby=['sepal_length', 'sepal_width'])"
 329 |    ]
 330 |   },
 331 |   {
 332 |    "cell_type": "markdown",
 333 |    "metadata": {},
 334 |    "source": [
 335 |     "Set additional parameters."
 336 |    ]
 337 |   },
 338 |   {
 339 |    "cell_type": "code",
 340 |    "execution_count": 9,
 341 |    "metadata": {
 342 |     "collapsed": false
 343 |    },
 344 |    "outputs": [
 345 |     {
 346 |      "data": {
 347 |       "text/plain": [
 348 |        "?.table.Fetch(table=dict(caslib='casuser', name='data.iris'), to=5)"
 349 |       ]
 350 |      },
 351 |      "execution_count": 9,
 352 |      "metadata": {},
 353 |      "output_type": "execute_result"
 354 |     }
 355 |    ],
 356 |    "source": [
 357 |     "fa.set_params('table', dict(name='data.iris', caslib='casuser'),\n",
 358 |     "              'to', 5)\n",
 359 |     "fa"
 360 |    ]
 361 |   },
 362 |   {
 363 |    "cell_type": "code",
 364 |    "execution_count": 10,
 365 |    "metadata": {
 366 |     "collapsed": false
 367 |    },
 368 |    "outputs": [
 369 |     {
 370 |      "data": {
 371 |       "text/html": [
 372 |        "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
 373 |        "<div class=\"cas-results-body\">\n",
 374 |        "<div>\n",
 375 |        "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table DATA.IRIS</caption>\n",
 376 |        "  <thead>\n",
 377 |        "    <tr style=\"text-align: right;\">\n",
 378 |        "      <th title=\"\"></th>\n",
 379 |        "      <th title=\"sepal_length\">sepal_length</th>\n",
 380 |        "      <th title=\"sepal_width\">sepal_width</th>\n",
 381 |        "      <th title=\"petal_length\">petal_length</th>\n",
 382 |        "      <th title=\"petal_width\">petal_width</th>\n",
 383 |        "      <th title=\"species\">species</th>\n",
 384 |        "    </tr>\n",
 385 |        "  </thead>\n",
 386 |        "  <tbody>\n",
 387 |        "    <tr>\n",
 388 |        "      <th>0</th>\n",
 389 |        "      <td>5.1</td>\n",
 390 |        "      <td>3.5</td>\n",
 391 |        "      <td>1.4</td>\n",
 392 |        "      <td>0.2</td>\n",
 393 |        "      <td>setosa</td>\n",
 394 |        "    </tr>\n",
 395 |        "    <tr>\n",
 396 |        "      <th>1</th>\n",
 397 |        "      <td>4.9</td>\n",
 398 |        "      <td>3.0</td>\n",
 399 |        "      <td>1.4</td>\n",
 400 |        "      <td>0.2</td>\n",
 401 |        "      <td>setosa</td>\n",
 402 |        "    </tr>\n",
 403 |        "    <tr>\n",
 404 |        "      <th>2</th>\n",
 405 |        "      <td>4.7</td>\n",
 406 |        "      <td>3.2</td>\n",
 407 |        "      <td>1.3</td>\n",
 408 |        "      <td>0.2</td>\n",
 409 |        "      <td>setosa</td>\n",
 410 |        "    </tr>\n",
 411 |        "    <tr>\n",
 412 |        "      <th>3</th>\n",
 413 |        "      <td>4.6</td>\n",
 414 |        "      <td>3.1</td>\n",
 415 |        "      <td>1.5</td>\n",
 416 |        "      <td>0.2</td>\n",
 417 |        "      <td>setosa</td>\n",
 418 |        "    </tr>\n",
 419 |        "    <tr>\n",
 420 |        "      <th>4</th>\n",
 421 |        "      <td>5.0</td>\n",
 422 |        "      <td>3.6</td>\n",
 423 |        "      <td>1.4</td>\n",
 424 |        "      <td>0.2</td>\n",
 425 |        "      <td>setosa</td>\n",
 426 |        "    </tr>\n",
 427 |        "  </tbody>\n",
 428 |        "</table>\n",
 429 |        "</div>\n",
 430 |        "</div>\n",
 431 |        "<div class=\"cas-output-area\"></div>\n",
 432 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00335s</span> &#183; <span class=\"cas-user\">user 0.003s</span> &#183; <span class=\"cas-sys\">sys 0.001s</span> &#183; <span class=\"cas-memory\">mem 1.64MB</span></small></p>"
 433 |       ],
 434 |       "text/plain": [
 435 |        "[Fetch]\n",
 436 |        "\n",
 437 |        " Selected Rows from Table DATA.IRIS\n",
 438 |        " \n",
 439 |        "    sepal_length  sepal_width  petal_length  petal_width species\n",
 440 |        " 0           5.1          3.5           1.4          0.2  setosa\n",
 441 |        " 1           4.9          3.0           1.4          0.2  setosa\n",
 442 |        " 2           4.7          3.2           1.3          0.2  setosa\n",
 443 |        " 3           4.6          3.1           1.5          0.2  setosa\n",
 444 |        " 4           5.0          3.6           1.4          0.2  setosa\n",
 445 |        "\n",
 446 |        "+ Elapsed: 0.00335s, user: 0.003s, sys: 0.001s, mem: 1.64mb"
 447 |       ]
 448 |      },
 449 |      "execution_count": 10,
 450 |      "metadata": {},
 451 |      "output_type": "execute_result"
 452 |     }
 453 |    ],
 454 |    "source": [
 455 |     "fa()"
 456 |    ]
 457 |   },
 458 |   {
 459 |    "cell_type": "markdown",
 460 |    "metadata": {},
 461 |    "source": [
 462 |     "Alternative ways of setting parameters.  These are all equivalent."
 463 |    ]
 464 |   },
 465 |   {
 466 |    "cell_type": "code",
 467 |    "execution_count": 13,
 468 |    "metadata": {
 469 |     "collapsed": false
 470 |    },
 471 |    "outputs": [
 472 |     {
 473 |      "data": {
 474 |       "text/plain": [
 475 |        "?.table.Fetch(table=dict(caslib='casuser', name='data.iris'), to=5)"
 476 |       ]
 477 |      },
 478 |      "execution_count": 13,
 479 |      "metadata": {},
 480 |      "output_type": "execute_result"
 481 |     }
 482 |    ],
 483 |    "source": [
 484 |     "fa.set_params(('table', dict(name='data.iris', caslib='casuser')),\n",
 485 |     "              ('to', 5))\n",
 486 |     "fa"
 487 |    ]
 488 |   },
 489 |   {
 490 |    "cell_type": "code",
 491 |    "execution_count": 14,
 492 |    "metadata": {
 493 |     "collapsed": false
 494 |    },
 495 |    "outputs": [
 496 |     {
 497 |      "data": {
 498 |       "text/plain": [
 499 |        "?.table.Fetch(table=dict(caslib='casuser', name='data.iris'), to=5)"
 500 |       ]
 501 |      },
 502 |      "execution_count": 14,
 503 |      "metadata": {},
 504 |      "output_type": "execute_result"
 505 |     }
 506 |    ],
 507 |    "source": [
 508 |     "fa.set_params({'table': dict(name='data.iris', caslib='casuser'),\n",
 509 |     "               'to': 5})\n",
 510 |     "fa"
 511 |    ]
 512 |   },
 513 |   {
 514 |    "cell_type": "code",
 515 |    "execution_count": 15,
 516 |    "metadata": {
 517 |     "collapsed": false
 518 |    },
 519 |    "outputs": [
 520 |     {
 521 |      "data": {
 522 |       "text/plain": [
 523 |        "?.table.Fetch(table=dict(caslib='casuser', name='data.iris'), to=5)"
 524 |       ]
 525 |      },
 526 |      "execution_count": 15,
 527 |      "metadata": {},
 528 |      "output_type": "execute_result"
 529 |     }
 530 |    ],
 531 |    "source": [
 532 |     "fa.set_params(table=dict(name='data.iris', caslib='casuser'),\n",
 533 |     "              to=5)\n",
 534 |     "fa"
 535 |    ]
 536 |   },
 537 |   {
 538 |    "cell_type": "markdown",
 539 |    "metadata": {},
 540 |    "source": [
 541 |     "### Setting Nested Parameters"
 542 |    ]
 543 |   },
 544 |   {
 545 |    "cell_type": "code",
 546 |    "execution_count": 16,
 547 |    "metadata": {
 548 |     "collapsed": true
 549 |    },
 550 |    "outputs": [],
 551 |    "source": [
 552 |     "fa = conn.Fetch()"
 553 |    ]
 554 |   },
 555 |   {
 556 |    "cell_type": "code",
 557 |    "execution_count": 17,
 558 |    "metadata": {
 559 |     "collapsed": false
 560 |    },
 561 |    "outputs": [
 562 |     {
 563 |      "data": {
 564 |       "text/plain": [
 565 |        "?.table.Fetch(table=dict(caslib='casuser', name='data.iris'))"
 566 |       ]
 567 |      },
 568 |      "execution_count": 17,
 569 |      "metadata": {},
 570 |      "output_type": "execute_result"
 571 |     }
 572 |    ],
 573 |    "source": [
 574 |     "fa.set_params('table.name', 'data.iris',\n",
 575 |     "              'table.caslib', 'casuser')\n",
 576 |     "fa"
 577 |    ]
 578 |   },
 579 |   {
 580 |    "cell_type": "code",
 581 |    "execution_count": 18,
 582 |    "metadata": {
 583 |     "collapsed": false
 584 |    },
 585 |    "outputs": [
 586 |     {
 587 |      "data": {
 588 |       "text/plain": [
 589 |        "?.table.Fetch(sortby=dict(0=dict(formatted='raw', name='petal_length'), 1=dict(formatted='raw', name='petal_width')), table=dict(caslib='casuser', name='data.iris'))"
 590 |       ]
 591 |      },
 592 |      "execution_count": 18,
 593 |      "metadata": {},
 594 |      "output_type": "execute_result"
 595 |     }
 596 |    ],
 597 |    "source": [
 598 |     "fa.set_params('sortby.0.name', 'petal_length',\n",
 599 |     "              'sortby.0.formatted', 'raw',\n",
 600 |     "              'sortby.1.name', 'petal_width',\n",
 601 |     "              'sortby.1.formatted', 'raw')\n",
 602 |     "fa"
 603 |    ]
 604 |   },
 605 |   {
 606 |    "cell_type": "code",
 607 |    "execution_count": 19,
 608 |    "metadata": {
 609 |     "collapsed": false
 610 |    },
 611 |    "outputs": [
 612 |     {
 613 |      "data": {
 614 |       "text/html": [
 615 |        "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
 616 |        "<div class=\"cas-results-body\">\n",
 617 |        "<div>\n",
 618 |        "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table DATA.IRIS</caption>\n",
 619 |        "  <thead>\n",
 620 |        "    <tr style=\"text-align: right;\">\n",
 621 |        "      <th title=\"\"></th>\n",
 622 |        "      <th title=\"sepal_length\">sepal_length</th>\n",
 623 |        "      <th title=\"sepal_width\">sepal_width</th>\n",
 624 |        "      <th title=\"petal_length\">petal_length</th>\n",
 625 |        "      <th title=\"petal_width\">petal_width</th>\n",
 626 |        "      <th title=\"species\">species</th>\n",
 627 |        "    </tr>\n",
 628 |        "  </thead>\n",
 629 |        "  <tbody>\n",
 630 |        "    <tr>\n",
 631 |        "      <th>0</th>\n",
 632 |        "      <td>4.6</td>\n",
 633 |        "      <td>3.6</td>\n",
 634 |        "      <td>1.0</td>\n",
 635 |        "      <td>0.2</td>\n",
 636 |        "      <td>setosa</td>\n",
 637 |        "    </tr>\n",
 638 |        "    <tr>\n",
 639 |        "      <th>1</th>\n",
 640 |        "      <td>4.3</td>\n",
 641 |        "      <td>3.0</td>\n",
 642 |        "      <td>1.1</td>\n",
 643 |        "      <td>0.1</td>\n",
 644 |        "      <td>setosa</td>\n",
 645 |        "    </tr>\n",
 646 |        "    <tr>\n",
 647 |        "      <th>2</th>\n",
 648 |        "      <td>5.0</td>\n",
 649 |        "      <td>3.2</td>\n",
 650 |        "      <td>1.2</td>\n",
 651 |        "      <td>0.2</td>\n",
 652 |        "      <td>setosa</td>\n",
 653 |        "    </tr>\n",
 654 |        "    <tr>\n",
 655 |        "      <th>3</th>\n",
 656 |        "      <td>5.8</td>\n",
 657 |        "      <td>4.0</td>\n",
 658 |        "      <td>1.2</td>\n",
 659 |        "      <td>0.2</td>\n",
 660 |        "      <td>setosa</td>\n",
 661 |        "    </tr>\n",
 662 |        "    <tr>\n",
 663 |        "      <th>4</th>\n",
 664 |        "      <td>4.7</td>\n",
 665 |        "      <td>3.2</td>\n",
 666 |        "      <td>1.3</td>\n",
 667 |        "      <td>0.2</td>\n",
 668 |        "      <td>setosa</td>\n",
 669 |        "    </tr>\n",
 670 |        "  </tbody>\n",
 671 |        "</table>\n",
 672 |        "</div>\n",
 673 |        "</div>\n",
 674 |        "<div class=\"cas-output-area\"></div>\n",
 675 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0134s</span> &#183; <span class=\"cas-user\">user 0.011s</span> &#183; <span class=\"cas-sys\">sys 0.002s</span> &#183; <span class=\"cas-memory\">mem 8.58MB</span></small></p>"
 676 |       ],
 677 |       "text/plain": [
 678 |        "[Fetch]\n",
 679 |        "\n",
 680 |        " Selected Rows from Table DATA.IRIS\n",
 681 |        " \n",
 682 |        "    sepal_length  sepal_width  petal_length  petal_width species\n",
 683 |        " 0           4.6          3.6           1.0          0.2  setosa\n",
 684 |        " 1           4.3          3.0           1.1          0.1  setosa\n",
 685 |        " 2           5.0          3.2           1.2          0.2  setosa\n",
 686 |        " 3           5.8          4.0           1.2          0.2  setosa\n",
 687 |        " 4           4.7          3.2           1.3          0.2  setosa\n",
 688 |        "\n",
 689 |        "+ Elapsed: 0.0134s, user: 0.011s, sys: 0.002s, mem: 8.58mb"
 690 |       ]
 691 |      },
 692 |      "execution_count": 19,
 693 |      "metadata": {},
 694 |      "output_type": "execute_result"
 695 |     }
 696 |    ],
 697 |    "source": [
 698 |     "fa(to=5)"
 699 |    ]
 700 |   },
 701 |   {
 702 |    "cell_type": "markdown",
 703 |    "metadata": {},
 704 |    "source": [
 705 |     "### Setting Parameters as Attributes"
 706 |    ]
 707 |   },
 708 |   {
 709 |    "cell_type": "code",
 710 |    "execution_count": 20,
 711 |    "metadata": {
 712 |     "collapsed": true
 713 |    },
 714 |    "outputs": [],
 715 |    "source": [
 716 |     "fa = conn.Fetch()"
 717 |    ]
 718 |   },
 719 |   {
 720 |    "cell_type": "code",
 721 |    "execution_count": 21,
 722 |    "metadata": {
 723 |     "collapsed": false
 724 |    },
 725 |    "outputs": [
 726 |     {
 727 |      "data": {
 728 |       "text/plain": [
 729 |        "?.table.Fetch(table=dict(caslib='casuser', name='data.iris'))"
 730 |       ]
 731 |      },
 732 |      "execution_count": 21,
 733 |      "metadata": {},
 734 |      "output_type": "execute_result"
 735 |     }
 736 |    ],
 737 |    "source": [
 738 |     "fa.params.table.name = 'data.iris'\n",
 739 |     "fa.params.table.caslib = 'casuser'\n",
 740 |     "fa"
 741 |    ]
 742 |   },
 743 |   {
 744 |    "cell_type": "code",
 745 |    "execution_count": 22,
 746 |    "metadata": {
 747 |     "collapsed": false
 748 |    },
 749 |    "outputs": [
 750 |     {
 751 |      "data": {
 752 |       "text/plain": [
 753 |        "?.table.Fetch(sortby=[dict(formatted='raw', name='petal_width'), dict(formatted='raw', name='petal_length')], table=dict(caslib='casuser', name='data.iris'))"
 754 |       ]
 755 |      },
 756 |      "execution_count": 22,
 757 |      "metadata": {},
 758 |      "output_type": "execute_result"
 759 |     }
 760 |    ],
 761 |    "source": [
 762 |     "fa.params.sortby[0].name = 'petal_width'\n",
 763 |     "fa.params.sortby[0].formatted = 'raw'\n",
 764 |     "fa.params.sortby[1].name = 'petal_length'\n",
 765 |     "fa.params.sortby[1].formatted = 'raw'\n",
 766 |     "fa"
 767 |    ]
 768 |   },
 769 |   {
 770 |    "cell_type": "markdown",
 771 |    "metadata": {},
 772 |    "source": [
 773 |     "You can save typing by creating a sortby variable first."
 774 |    ]
 775 |   },
 776 |   {
 777 |    "cell_type": "code",
 778 |    "execution_count": 25,
 779 |    "metadata": {
 780 |     "collapsed": false
 781 |    },
 782 |    "outputs": [
 783 |     {
 784 |      "data": {
 785 |       "text/plain": [
 786 |        "{0: {'formatted': 'raw', 'name': 'petal_width'},\n",
 787 |        " 1: {'formatted': 'raw', 'name': 'petal_length'}}"
 788 |       ]
 789 |      },
 790 |      "execution_count": 25,
 791 |      "metadata": {},
 792 |      "output_type": "execute_result"
 793 |     }
 794 |    ],
 795 |    "source": [
 796 |     "sortby = fa.params.table.sortby\n",
 797 |     "sortby[0].name = 'petal_width'\n",
 798 |     "sortby[0].formatted = 'raw'\n",
 799 |     "sortby[1].name = 'petal_length'\n",
 800 |     "sortby[1].formatted = 'raw'\n",
 801 |     "sortby"
 802 |    ]
 803 |   },
 804 |   {
 805 |    "cell_type": "markdown",
 806 |    "metadata": {},
 807 |    "source": [
 808 |     "Be careful of name collisions with dictionary method names!"
 809 |    ]
 810 |   },
 811 |   {
 812 |    "cell_type": "code",
 813 |    "execution_count": 29,
 814 |    "metadata": {
 815 |     "collapsed": false
 816 |    },
 817 |    "outputs": [],
 818 |    "source": [
 819 |     "fa.params.pop = 'corn'"
 820 |    ]
 821 |   },
 822 |   {
 823 |    "cell_type": "markdown",
 824 |    "metadata": {},
 825 |    "source": [
 826 |     "This returns the dictionary method, not the value we just added to the parameters."
 827 |    ]
 828 |   },
 829 |   {
 830 |    "cell_type": "code",
 831 |    "execution_count": 31,
 832 |    "metadata": {
 833 |     "collapsed": false
 834 |    },
 835 |    "outputs": [
 836 |     {
 837 |      "data": {
 838 |       "text/plain": [
 839 |        "<bound method xadict.pop of {'sortby': {0: {'name': 'petal_width', 'formatted': 'raw'}, 1: {'name': 'petal_length', 'formatted': 'raw'}}, 'pop': 'corn', 'table': {'sortby': {0: {'name': 'petal_width', 'formatted': 'raw'}, 1: {'name': 'petal_length', 'formatted': 'raw'}}, 'name': 'data.iris', 'caslib': 'casuser'}}>"
 840 |       ]
 841 |      },
 842 |      "execution_count": 31,
 843 |      "metadata": {},
 844 |      "output_type": "execute_result"
 845 |     }
 846 |    ],
 847 |    "source": [
 848 |     "fa.params.pop"
 849 |    ]
 850 |   },
 851 |   {
 852 |    "cell_type": "markdown",
 853 |    "metadata": {},
 854 |    "source": [
 855 |     "Using dictionary syntax will return the proper value though."
 856 |    ]
 857 |   },
 858 |   {
 859 |    "cell_type": "code",
 860 |    "execution_count": 33,
 861 |    "metadata": {
 862 |     "collapsed": false
 863 |    },
 864 |    "outputs": [
 865 |     {
 866 |      "data": {
 867 |       "text/plain": [
 868 |        "'corn'"
 869 |       ]
 870 |      },
 871 |      "execution_count": 33,
 872 |      "metadata": {},
 873 |      "output_type": "execute_result"
 874 |     }
 875 |    ],
 876 |    "source": [
 877 |     "fa.params['pop']"
 878 |    ]
 879 |   },
 880 |   {
 881 |    "cell_type": "markdown",
 882 |    "metadata": {},
 883 |    "source": [
 884 |     "Parameters can also be set directly on the **CASAction** instance. "
 885 |    ]
 886 |   },
 887 |   {
 888 |    "cell_type": "code",
 889 |    "execution_count": 35,
 890 |    "metadata": {
 891 |     "collapsed": false
 892 |    },
 893 |    "outputs": [],
 894 |    "source": [
 895 |     "fa = conn.Fetch()"
 896 |    ]
 897 |   },
 898 |   {
 899 |    "cell_type": "code",
 900 |    "execution_count": 36,
 901 |    "metadata": {
 902 |     "collapsed": false
 903 |    },
 904 |    "outputs": [
 905 |     {
 906 |      "data": {
 907 |       "text/plain": [
 908 |        "?.table.Fetch(table=dict(caslib='casuser', name='data.iris'))"
 909 |       ]
 910 |      },
 911 |      "execution_count": 36,
 912 |      "metadata": {},
 913 |      "output_type": "execute_result"
 914 |     }
 915 |    ],
 916 |    "source": [
 917 |     "fa.table.name = 'data.iris'\n",
 918 |     "fa.table.caslib = 'casuser'\n",
 919 |     "fa"
 920 |    ]
 921 |   },
 922 |   {
 923 |    "cell_type": "code",
 924 |    "execution_count": 37,
 925 |    "metadata": {
 926 |     "collapsed": false
 927 |    },
 928 |    "outputs": [
 929 |     {
 930 |      "data": {
 931 |       "text/html": [
 932 |        "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
 933 |        "<div class=\"cas-results-body\">\n",
 934 |        "<div>\n",
 935 |        "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table DATA.IRIS</caption>\n",
 936 |        "  <thead>\n",
 937 |        "    <tr style=\"text-align: right;\">\n",
 938 |        "      <th title=\"\"></th>\n",
 939 |        "      <th title=\"sepal_length\">sepal_length</th>\n",
 940 |        "      <th title=\"sepal_width\">sepal_width</th>\n",
 941 |        "      <th title=\"petal_length\">petal_length</th>\n",
 942 |        "      <th title=\"petal_width\">petal_width</th>\n",
 943 |        "      <th title=\"species\">species</th>\n",
 944 |        "    </tr>\n",
 945 |        "  </thead>\n",
 946 |        "  <tbody>\n",
 947 |        "    <tr>\n",
 948 |        "      <th>0</th>\n",
 949 |        "      <td>5.1</td>\n",
 950 |        "      <td>3.5</td>\n",
 951 |        "      <td>1.4</td>\n",
 952 |        "      <td>0.2</td>\n",
 953 |        "      <td>setosa</td>\n",
 954 |        "    </tr>\n",
 955 |        "    <tr>\n",
 956 |        "      <th>1</th>\n",
 957 |        "      <td>4.9</td>\n",
 958 |        "      <td>3.0</td>\n",
 959 |        "      <td>1.4</td>\n",
 960 |        "      <td>0.2</td>\n",
 961 |        "      <td>setosa</td>\n",
 962 |        "    </tr>\n",
 963 |        "    <tr>\n",
 964 |        "      <th>2</th>\n",
 965 |        "      <td>4.7</td>\n",
 966 |        "      <td>3.2</td>\n",
 967 |        "      <td>1.3</td>\n",
 968 |        "      <td>0.2</td>\n",
 969 |        "      <td>setosa</td>\n",
 970 |        "    </tr>\n",
 971 |        "    <tr>\n",
 972 |        "      <th>3</th>\n",
 973 |        "      <td>4.6</td>\n",
 974 |        "      <td>3.1</td>\n",
 975 |        "      <td>1.5</td>\n",
 976 |        "      <td>0.2</td>\n",
 977 |        "      <td>setosa</td>\n",
 978 |        "    </tr>\n",
 979 |        "    <tr>\n",
 980 |        "      <th>4</th>\n",
 981 |        "      <td>5.0</td>\n",
 982 |        "      <td>3.6</td>\n",
 983 |        "      <td>1.4</td>\n",
 984 |        "      <td>0.2</td>\n",
 985 |        "      <td>setosa</td>\n",
 986 |        "    </tr>\n",
 987 |        "  </tbody>\n",
 988 |        "</table>\n",
 989 |        "</div>\n",
 990 |        "</div>\n",
 991 |        "<div class=\"cas-output-area\"></div>\n",
 992 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00336s</span> &#183; <span class=\"cas-user\">user 0.003s</span> &#183; <span class=\"cas-memory\">mem 1.64MB</span></small></p>"
 993 |       ],
 994 |       "text/plain": [
 995 |        "[Fetch]\n",
 996 |        "\n",
 997 |        " Selected Rows from Table DATA.IRIS\n",
 998 |        " \n",
 999 |        "    sepal_length  sepal_width  petal_length  petal_width species\n",
1000 |        " 0           5.1          3.5           1.4          0.2  setosa\n",
1001 |        " 1           4.9          3.0           1.4          0.2  setosa\n",
1002 |        " 2           4.7          3.2           1.3          0.2  setosa\n",
1003 |        " 3           4.6          3.1           1.5          0.2  setosa\n",
1004 |        " 4           5.0          3.6           1.4          0.2  setosa\n",
1005 |        "\n",
1006 |        "+ Elapsed: 0.00336s, user: 0.003s, mem: 1.64mb"
1007 |       ]
1008 |      },
1009 |      "execution_count": 37,
1010 |      "metadata": {},
1011 |      "output_type": "execute_result"
1012 |     }
1013 |    ],
1014 |    "source": [
1015 |     "fa(to=5)"
1016 |    ]
1017 |   },
1018 |   {
1019 |    "cell_type": "markdown",
1020 |    "metadata": {},
1021 |    "source": [
1022 |     "### Retrieving and Removing Action Parameters"
1023 |    ]
1024 |   },
1025 |   {
1026 |    "cell_type": "code",
1027 |    "execution_count": 39,
1028 |    "metadata": {
1029 |     "collapsed": false
1030 |    },
1031 |    "outputs": [
1032 |     {
1033 |      "data": {
1034 |       "text/plain": [
1035 |        "?.table.Fetch(table=dict(caslib='casuser', name='data.iris'), to=5)"
1036 |       ]
1037 |      },
1038 |      "execution_count": 39,
1039 |      "metadata": {},
1040 |      "output_type": "execute_result"
1041 |     }
1042 |    ],
1043 |    "source": [
1044 |     "fa = conn.Fetch(to=5, table=dict(name='data.iris', caslib='casuser'))\n",
1045 |     "fa"
1046 |    ]
1047 |   },
1048 |   {
1049 |    "cell_type": "code",
1050 |    "execution_count": 40,
1051 |    "metadata": {
1052 |     "collapsed": false
1053 |    },
1054 |    "outputs": [
1055 |     {
1056 |      "data": {
1057 |       "text/plain": [
1058 |        "5"
1059 |       ]
1060 |      },
1061 |      "execution_count": 40,
1062 |      "metadata": {},
1063 |      "output_type": "execute_result"
1064 |     }
1065 |    ],
1066 |    "source": [
1067 |     "fa.get_param('to')"
1068 |    ]
1069 |   },
1070 |   {
1071 |    "cell_type": "code",
1072 |    "execution_count": 41,
1073 |    "metadata": {
1074 |     "collapsed": false
1075 |    },
1076 |    "outputs": [
1077 |     {
1078 |      "data": {
1079 |       "text/plain": [
1080 |        "{'table.name': 'data.iris', 'to': 5}"
1081 |       ]
1082 |      },
1083 |      "execution_count": 41,
1084 |      "metadata": {},
1085 |      "output_type": "execute_result"
1086 |     }
1087 |    ],
1088 |    "source": [
1089 |     "fa.get_params('to', 'table.name')"
1090 |    ]
1091 |   },
1092 |   {
1093 |    "cell_type": "code",
1094 |    "execution_count": 43,
1095 |    "metadata": {
1096 |     "collapsed": false
1097 |    },
1098 |    "outputs": [
1099 |     {
1100 |      "data": {
1101 |       "text/plain": [
1102 |        "?.table.Fetch(table=dict(name='data.iris'))"
1103 |       ]
1104 |      },
1105 |      "execution_count": 43,
1106 |      "metadata": {},
1107 |      "output_type": "execute_result"
1108 |     }
1109 |    ],
1110 |    "source": [
1111 |     "fa.del_params('to', 'table.caslib')\n",
1112 |     "fa"
1113 |    ]
1114 |   },
1115 |   {
1116 |    "cell_type": "code",
1117 |    "execution_count": 44,
1118 |    "metadata": {
1119 |     "collapsed": false
1120 |    },
1121 |    "outputs": [
1122 |     {
1123 |      "data": {
1124 |       "text/plain": [
1125 |        "False"
1126 |       ]
1127 |      },
1128 |      "execution_count": 44,
1129 |      "metadata": {},
1130 |      "output_type": "execute_result"
1131 |     }
1132 |    ],
1133 |    "source": [
1134 |     "fa.has_param('table.caslib')"
1135 |    ]
1136 |   },
1137 |   {
1138 |    "cell_type": "code",
1139 |    "execution_count": 45,
1140 |    "metadata": {
1141 |     "collapsed": false
1142 |    },
1143 |    "outputs": [
1144 |     {
1145 |      "data": {
1146 |       "text/plain": [
1147 |        "True"
1148 |       ]
1149 |      },
1150 |      "execution_count": 45,
1151 |      "metadata": {},
1152 |      "output_type": "execute_result"
1153 |     }
1154 |    ],
1155 |    "source": [
1156 |     "fa.has_param('table.name')"
1157 |    ]
1158 |   },
1159 |   {
1160 |    "cell_type": "code",
1161 |    "execution_count": 46,
1162 |    "metadata": {
1163 |     "collapsed": false
1164 |    },
1165 |    "outputs": [
1166 |     {
1167 |      "data": {
1168 |       "text/plain": [
1169 |        "False"
1170 |       ]
1171 |      },
1172 |      "execution_count": 46,
1173 |      "metadata": {},
1174 |      "output_type": "execute_result"
1175 |     }
1176 |    ],
1177 |    "source": [
1178 |     "fa.has_param('table.name', 'to')"
1179 |    ]
1180 |   },
1181 |   {
1182 |    "cell_type": "markdown",
1183 |    "metadata": {},
1184 |    "source": [
1185 |     "Retrieve parameter values using attributes."
1186 |    ]
1187 |   },
1188 |   {
1189 |    "cell_type": "code",
1190 |    "execution_count": 47,
1191 |    "metadata": {
1192 |     "collapsed": false
1193 |    },
1194 |    "outputs": [
1195 |     {
1196 |      "data": {
1197 |       "text/plain": [
1198 |        "?.table.Fetch(table=dict(caslib='casuser', name='data.iris'), to=5)"
1199 |       ]
1200 |      },
1201 |      "execution_count": 47,
1202 |      "metadata": {},
1203 |      "output_type": "execute_result"
1204 |     }
1205 |    ],
1206 |    "source": [
1207 |     "fa = conn.Fetch(to=5, table=dict(name='data.iris', caslib='casuser'))\n",
1208 |     "fa"
1209 |    ]
1210 |   },
1211 |   {
1212 |    "cell_type": "code",
1213 |    "execution_count": 48,
1214 |    "metadata": {
1215 |     "collapsed": false
1216 |    },
1217 |    "outputs": [
1218 |     {
1219 |      "data": {
1220 |       "text/plain": [
1221 |        "'data.iris'"
1222 |       ]
1223 |      },
1224 |      "execution_count": 48,
1225 |      "metadata": {},
1226 |      "output_type": "execute_result"
1227 |     }
1228 |    ],
1229 |    "source": [
1230 |     "fa.table.name"
1231 |    ]
1232 |   },
1233 |   {
1234 |    "cell_type": "code",
1235 |    "execution_count": 49,
1236 |    "metadata": {
1237 |     "collapsed": false
1238 |    },
1239 |    "outputs": [
1240 |     {
1241 |      "data": {
1242 |       "text/plain": [
1243 |        "{'caslib': 'casuser', 'name': 'data.iris'}"
1244 |       ]
1245 |      },
1246 |      "execution_count": 49,
1247 |      "metadata": {},
1248 |      "output_type": "execute_result"
1249 |     }
1250 |    ],
1251 |    "source": [
1252 |     "fa.table"
1253 |    ]
1254 |   },
1255 |   {
1256 |    "cell_type": "code",
1257 |    "execution_count": 50,
1258 |    "metadata": {
1259 |     "collapsed": true
1260 |    },
1261 |    "outputs": [],
1262 |    "source": [
1263 |     "del fa.table.caslib"
1264 |    ]
1265 |   },
1266 |   {
1267 |    "cell_type": "code",
1268 |    "execution_count": 51,
1269 |    "metadata": {
1270 |     "collapsed": false
1271 |    },
1272 |    "outputs": [
1273 |     {
1274 |      "data": {
1275 |       "text/plain": [
1276 |        "{'name': 'data.iris'}"
1277 |       ]
1278 |      },
1279 |      "execution_count": 51,
1280 |      "metadata": {},
1281 |      "output_type": "execute_result"
1282 |     }
1283 |    ],
1284 |    "source": [
1285 |     "fa.table"
1286 |    ]
1287 |   },
1288 |   {
1289 |    "cell_type": "markdown",
1290 |    "metadata": {},
1291 |    "source": [
1292 |     "## First Steps with the CASTable Object"
1293 |    ]
1294 |   },
1295 |   {
1296 |    "cell_type": "code",
1297 |    "execution_count": 52,
1298 |    "metadata": {
1299 |     "collapsed": false
1300 |    },
1301 |    "outputs": [
1302 |     {
1303 |      "name": "stderr",
1304 |      "output_type": "stream",
1305 |      "text": [
1306 |       "ERROR: The table DATA.IRIS already exists in caslib CASUSER(username).\n",
1307 |       "ERROR: The action stopped due to errors.\n"
1308 |      ]
1309 |     },
1310 |     {
1311 |      "data": {
1312 |       "text/html": [
1313 |        "<div class=\"cas-results-key\"><b>&#167; caslib</b></div>\n",
1314 |        "<div class=\"cas-results-body\">\n",
1315 |        "<div>CASUSER(username)</div>\n",
1316 |        "</div>\n",
1317 |        "<div class=\"cas-results-key\"><hr/><b>&#167; tableName</b></div>\n",
1318 |        "<div class=\"cas-results-body\">\n",
1319 |        "<div>DATA.IRIS</div>\n",
1320 |        "</div>\n",
1321 |        "<div class=\"cas-results-key\"><hr/><b>&#167; casTable</b></div>\n",
1322 |        "<div class=\"cas-results-body\">\n",
1323 |        "<div>CASTable('DATA.IRIS', caslib='CASUSER(username)')</div>\n",
1324 |        "</div>\n",
1325 |        "<div class=\"cas-output-area\"></div>\n",
1326 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.000472s</span> &#183; <span class=\"cas-memory\">mem 0.123MB</span></small></p>"
1327 |       ],
1328 |       "text/plain": [
1329 |        "[caslib]\n",
1330 |        "\n",
1331 |        " 'CASUSER(username)'\n",
1332 |        "\n",
1333 |        "[tableName]\n",
1334 |        "\n",
1335 |        " 'DATA.IRIS'\n",
1336 |        "\n",
1337 |        "[casTable]\n",
1338 |        "\n",
1339 |        " CASTable('DATA.IRIS', caslib='CASUSER(username)')\n",
1340 |        "\n",
1341 |        "+ Elapsed: 0.000472s, mem: 0.123mb"
1342 |       ]
1343 |      },
1344 |      "execution_count": 52,
1345 |      "metadata": {},
1346 |      "output_type": "execute_result"
1347 |     }
1348 |    ],
1349 |    "source": [
1350 |     "out = conn.loadtable('data/iris.csv', caslib='casuser')\n",
1351 |     "out"
1352 |    ]
1353 |   },
1354 |   {
1355 |    "cell_type": "code",
1356 |    "execution_count": 53,
1357 |    "metadata": {
1358 |     "collapsed": false
1359 |    },
1360 |    "outputs": [
1361 |     {
1362 |      "data": {
1363 |       "text/plain": [
1364 |        "'DATA.IRIS'"
1365 |       ]
1366 |      },
1367 |      "execution_count": 53,
1368 |      "metadata": {},
1369 |      "output_type": "execute_result"
1370 |     }
1371 |    ],
1372 |    "source": [
1373 |     "out['tableName']"
1374 |    ]
1375 |   },
1376 |   {
1377 |    "cell_type": "code",
1378 |    "execution_count": 54,
1379 |    "metadata": {
1380 |     "collapsed": false
1381 |    },
1382 |    "outputs": [
1383 |     {
1384 |      "data": {
1385 |       "text/plain": [
1386 |        "'CASUSER(username)'"
1387 |       ]
1388 |      },
1389 |      "execution_count": 54,
1390 |      "metadata": {},
1391 |      "output_type": "execute_result"
1392 |     }
1393 |    ],
1394 |    "source": [
1395 |     "out['caslib']"
1396 |    ]
1397 |   },
1398 |   {
1399 |    "cell_type": "code",
1400 |    "execution_count": 55,
1401 |    "metadata": {
1402 |     "collapsed": false
1403 |    },
1404 |    "outputs": [
1405 |     {
1406 |      "data": {
1407 |       "text/plain": [
1408 |        "CASTable('DATA.IRIS', caslib='CASUSER(username)')"
1409 |       ]
1410 |      },
1411 |      "execution_count": 55,
1412 |      "metadata": {},
1413 |      "output_type": "execute_result"
1414 |     }
1415 |    ],
1416 |    "source": [
1417 |     "out['casTable']"
1418 |    ]
1419 |   },
1420 |   {
1421 |    "cell_type": "code",
1422 |    "execution_count": 56,
1423 |    "metadata": {
1424 |     "collapsed": false
1425 |    },
1426 |    "outputs": [
1427 |     {
1428 |      "data": {
1429 |       "text/plain": [
1430 |        "CASTable('DATA.IRIS', caslib='CASUSER(username)')"
1431 |       ]
1432 |      },
1433 |      "execution_count": 56,
1434 |      "metadata": {},
1435 |      "output_type": "execute_result"
1436 |     }
1437 |    ],
1438 |    "source": [
1439 |     "out.casTable"
1440 |    ]
1441 |   },
1442 |   {
1443 |    "cell_type": "code",
1444 |    "execution_count": 57,
1445 |    "metadata": {
1446 |     "collapsed": false
1447 |    },
1448 |    "outputs": [
1449 |     {
1450 |      "data": {
1451 |       "text/html": [
1452 |        "<div class=\"cas-results-key\"><b>&#167; TableInfo</b></div>\n",
1453 |        "<div class=\"cas-results-body\">\n",
1454 |        "<div>\n",
1455 |        "<table border=\"1\" class=\"dataframe\">\n",
1456 |        "  <thead>\n",
1457 |        "    <tr style=\"text-align: right;\">\n",
1458 |        "      <th title=\"\"></th>\n",
1459 |        "      <th title=\"Name\">Name</th>\n",
1460 |        "      <th title=\"Rows\">Rows</th>\n",
1461 |        "      <th title=\"Columns\">Columns</th>\n",
1462 |        "      <th title=\"Encoding\">Encoding</th>\n",
1463 |        "      <th title=\"Created\">CreateTimeFormatted</th>\n",
1464 |        "      <th title=\"Last Modified\">ModTimeFormatted</th>\n",
1465 |        "      <th title=\"Character Set\">JavaCharSet</th>\n",
1466 |        "      <th title=\"CreateTime\">CreateTime</th>\n",
1467 |        "      <th title=\"ModTime\">ModTime</th>\n",
1468 |        "      <th title=\"Global\">Global</th>\n",
1469 |        "      <th title=\"Repeated\">Repeated</th>\n",
1470 |        "      <th title=\"View\">View</th>\n",
1471 |        "      <th title=\"Loaded Source\">SourceName</th>\n",
1472 |        "      <th title=\"Source Caslib\">SourceCaslib</th>\n",
1473 |        "      <th title=\"Compressed\">Compressed</th>\n",
1474 |        "      <th title=\"Table Creator\">Creator</th>\n",
1475 |        "      <th title=\"Last Table Modifier\">Modifier</th>\n",
1476 |        "    </tr>\n",
1477 |        "  </thead>\n",
1478 |        "  <tbody>\n",
1479 |        "    <tr>\n",
1480 |        "      <th>0</th>\n",
1481 |        "      <td>DATA.IRIS</td>\n",
1482 |        "      <td>150</td>\n",
1483 |        "      <td>5</td>\n",
1484 |        "      <td>utf-8</td>\n",
1485 |        "      <td>03Nov2016:15:28:57</td>\n",
1486 |        "      <td>03Nov2016:15:28:57</td>\n",
1487 |        "      <td>UTF8</td>\n",
1488 |        "      <td>1.793806e+09</td>\n",
1489 |        "      <td>1.793806e+09</td>\n",
1490 |        "      <td>0</td>\n",
1491 |        "      <td>0</td>\n",
1492 |        "      <td>0</td>\n",
1493 |        "      <td>data/iris.csv</td>\n",
1494 |        "      <td>CASUSER(username)</td>\n",
1495 |        "      <td>0</td>\n",
1496 |        "      <td>username</td>\n",
1497 |        "      <td></td>\n",
1498 |        "    </tr>\n",
1499 |        "  </tbody>\n",
1500 |        "</table>\n",
1501 |        "</div>\n",
1502 |        "</div>\n",
1503 |        "<div class=\"cas-output-area\"></div>\n",
1504 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.000939s</span> &#183; <span class=\"cas-sys\">sys 0.001s</span> &#183; <span class=\"cas-memory\">mem 0.106MB</span></small></p>"
1505 |       ],
1506 |       "text/plain": [
1507 |        "[TableInfo]\n",
1508 |        "\n",
1509 |        "         Name  Rows  Columns Encoding CreateTimeFormatted    ModTimeFormatted  \\\n",
1510 |        " 0  DATA.IRIS   150        5    utf-8  03Nov2016:15:28:57  03Nov2016:15:28:57   \n",
1511 |        " \n",
1512 |        "   JavaCharSet    CreateTime       ModTime  Global  Repeated  View  \\\n",
1513 |        " 0        UTF8  1.793806e+09  1.793806e+09       0         0     0   \n",
1514 |        " \n",
1515 |        "       SourceName     SourceCaslib  Compressed Creator Modifier  \n",
1516 |        " 0  data/iris.csv  CASUSER(username)           0  username           \n",
1517 |        "\n",
1518 |        "+ Elapsed: 0.000939s, sys: 0.001s, mem: 0.106mb"
1519 |       ]
1520 |      },
1521 |      "execution_count": 57,
1522 |      "metadata": {},
1523 |      "output_type": "execute_result"
1524 |     }
1525 |    ],
1526 |    "source": [
1527 |     "out.casTable.tableinfo()"
1528 |    ]
1529 |   },
1530 |   {
1531 |    "cell_type": "code",
1532 |    "execution_count": 58,
1533 |    "metadata": {
1534 |     "collapsed": false
1535 |    },
1536 |    "outputs": [
1537 |     {
1538 |      "data": {
1539 |       "text/html": [
1540 |        "<div class=\"cas-results-key\"><b>&#167; ColumnInfo</b></div>\n",
1541 |        "<div class=\"cas-results-body\">\n",
1542 |        "<div>\n",
1543 |        "<table border=\"1\" class=\"dataframe\">\n",
1544 |        "  <thead>\n",
1545 |        "    <tr style=\"text-align: right;\">\n",
1546 |        "      <th title=\"\"></th>\n",
1547 |        "      <th title=\"Column\">Column</th>\n",
1548 |        "      <th title=\"ID\">ID</th>\n",
1549 |        "      <th title=\"Type\">Type</th>\n",
1550 |        "      <th title=\"RawLength\">RawLength</th>\n",
1551 |        "      <th title=\"FormattedLength\">FormattedLength</th>\n",
1552 |        "      <th title=\"NFL\">NFL</th>\n",
1553 |        "      <th title=\"NFD\">NFD</th>\n",
1554 |        "    </tr>\n",
1555 |        "  </thead>\n",
1556 |        "  <tbody>\n",
1557 |        "    <tr>\n",
1558 |        "      <th>0</th>\n",
1559 |        "      <td>sepal_length</td>\n",
1560 |        "      <td>1</td>\n",
1561 |        "      <td>double</td>\n",
1562 |        "      <td>8</td>\n",
1563 |        "      <td>12</td>\n",
1564 |        "      <td>0</td>\n",
1565 |        "      <td>0</td>\n",
1566 |        "    </tr>\n",
1567 |        "    <tr>\n",
1568 |        "      <th>1</th>\n",
1569 |        "      <td>sepal_width</td>\n",
1570 |        "      <td>2</td>\n",
1571 |        "      <td>double</td>\n",
1572 |        "      <td>8</td>\n",
1573 |        "      <td>12</td>\n",
1574 |        "      <td>0</td>\n",
1575 |        "      <td>0</td>\n",
1576 |        "    </tr>\n",
1577 |        "    <tr>\n",
1578 |        "      <th>2</th>\n",
1579 |        "      <td>petal_length</td>\n",
1580 |        "      <td>3</td>\n",
1581 |        "      <td>double</td>\n",
1582 |        "      <td>8</td>\n",
1583 |        "      <td>12</td>\n",
1584 |        "      <td>0</td>\n",
1585 |        "      <td>0</td>\n",
1586 |        "    </tr>\n",
1587 |        "    <tr>\n",
1588 |        "      <th>3</th>\n",
1589 |        "      <td>petal_width</td>\n",
1590 |        "      <td>4</td>\n",
1591 |        "      <td>double</td>\n",
1592 |        "      <td>8</td>\n",
1593 |        "      <td>12</td>\n",
1594 |        "      <td>0</td>\n",
1595 |        "      <td>0</td>\n",
1596 |        "    </tr>\n",
1597 |        "    <tr>\n",
1598 |        "      <th>4</th>\n",
1599 |        "      <td>species</td>\n",
1600 |        "      <td>5</td>\n",
1601 |        "      <td>varchar</td>\n",
1602 |        "      <td>10</td>\n",
1603 |        "      <td>10</td>\n",
1604 |        "      <td>0</td>\n",
1605 |        "      <td>0</td>\n",
1606 |        "    </tr>\n",
1607 |        "  </tbody>\n",
1608 |        "</table>\n",
1609 |        "</div>\n",
1610 |        "</div>\n",
1611 |        "<div class=\"cas-output-area\"></div>\n",
1612 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.000666s</span> &#183; <span class=\"cas-user\">user 0.001s</span> &#183; <span class=\"cas-memory\">mem 0.169MB</span></small></p>"
1613 |       ],
1614 |       "text/plain": [
1615 |        "[ColumnInfo]\n",
1616 |        "\n",
1617 |        "          Column  ID     Type  RawLength  FormattedLength  NFL  NFD\n",
1618 |        " 0  sepal_length   1   double          8               12    0    0\n",
1619 |        " 1   sepal_width   2   double          8               12    0    0\n",
1620 |        " 2  petal_length   3   double          8               12    0    0\n",
1621 |        " 3   petal_width   4   double          8               12    0    0\n",
1622 |        " 4       species   5  varchar         10               10    0    0\n",
1623 |        "\n",
1624 |        "+ Elapsed: 0.000666s, user: 0.001s, mem: 0.169mb"
1625 |       ]
1626 |      },
1627 |      "execution_count": 58,
1628 |      "metadata": {},
1629 |      "output_type": "execute_result"
1630 |     }
1631 |    ],
1632 |    "source": [
1633 |     "out.casTable.columninfo()"
1634 |    ]
1635 |   },
1636 |   {
1637 |    "cell_type": "code",
1638 |    "execution_count": 59,
1639 |    "metadata": {
1640 |     "collapsed": false
1641 |    },
1642 |    "outputs": [
1643 |     {
1644 |      "data": {
1645 |       "text/html": [
1646 |        "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
1647 |        "<div class=\"cas-results-body\">\n",
1648 |        "<div>\n",
1649 |        "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table DATA.IRIS</caption>\n",
1650 |        "  <thead>\n",
1651 |        "    <tr style=\"text-align: right;\">\n",
1652 |        "      <th title=\"\"></th>\n",
1653 |        "      <th title=\"sepal_length\">sepal_length</th>\n",
1654 |        "      <th title=\"sepal_width\">sepal_width</th>\n",
1655 |        "      <th title=\"petal_length\">petal_length</th>\n",
1656 |        "      <th title=\"petal_width\">petal_width</th>\n",
1657 |        "      <th title=\"species\">species</th>\n",
1658 |        "    </tr>\n",
1659 |        "  </thead>\n",
1660 |        "  <tbody>\n",
1661 |        "    <tr>\n",
1662 |        "      <th>0</th>\n",
1663 |        "      <td>5.1</td>\n",
1664 |        "      <td>3.5</td>\n",
1665 |        "      <td>1.4</td>\n",
1666 |        "      <td>0.2</td>\n",
1667 |        "      <td>setosa</td>\n",
1668 |        "    </tr>\n",
1669 |        "    <tr>\n",
1670 |        "      <th>1</th>\n",
1671 |        "      <td>4.9</td>\n",
1672 |        "      <td>3.0</td>\n",
1673 |        "      <td>1.4</td>\n",
1674 |        "      <td>0.2</td>\n",
1675 |        "      <td>setosa</td>\n",
1676 |        "    </tr>\n",
1677 |        "    <tr>\n",
1678 |        "      <th>2</th>\n",
1679 |        "      <td>4.7</td>\n",
1680 |        "      <td>3.2</td>\n",
1681 |        "      <td>1.3</td>\n",
1682 |        "      <td>0.2</td>\n",
1683 |        "      <td>setosa</td>\n",
1684 |        "    </tr>\n",
1685 |        "    <tr>\n",
1686 |        "      <th>3</th>\n",
1687 |        "      <td>4.6</td>\n",
1688 |        "      <td>3.1</td>\n",
1689 |        "      <td>1.5</td>\n",
1690 |        "      <td>0.2</td>\n",
1691 |        "      <td>setosa</td>\n",
1692 |        "    </tr>\n",
1693 |        "    <tr>\n",
1694 |        "      <th>4</th>\n",
1695 |        "      <td>5.0</td>\n",
1696 |        "      <td>3.6</td>\n",
1697 |        "      <td>1.4</td>\n",
1698 |        "      <td>0.2</td>\n",
1699 |        "      <td>setosa</td>\n",
1700 |        "    </tr>\n",
1701 |        "  </tbody>\n",
1702 |        "</table>\n",
1703 |        "</div>\n",
1704 |        "</div>\n",
1705 |        "<div class=\"cas-output-area\"></div>\n",
1706 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00335s</span> &#183; <span class=\"cas-user\">user 0.002s</span> &#183; <span class=\"cas-sys\">sys 0.001s</span> &#183; <span class=\"cas-memory\">mem 1.64MB</span></small></p>"
1707 |       ],
1708 |       "text/plain": [
1709 |        "[Fetch]\n",
1710 |        "\n",
1711 |        " Selected Rows from Table DATA.IRIS\n",
1712 |        " \n",
1713 |        "    sepal_length  sepal_width  petal_length  petal_width species\n",
1714 |        " 0           5.1          3.5           1.4          0.2  setosa\n",
1715 |        " 1           4.9          3.0           1.4          0.2  setosa\n",
1716 |        " 2           4.7          3.2           1.3          0.2  setosa\n",
1717 |        " 3           4.6          3.1           1.5          0.2  setosa\n",
1718 |        " 4           5.0          3.6           1.4          0.2  setosa\n",
1719 |        "\n",
1720 |        "+ Elapsed: 0.00335s, user: 0.002s, sys: 0.001s, mem: 1.64mb"
1721 |       ]
1722 |      },
1723 |      "execution_count": 59,
1724 |      "metadata": {},
1725 |      "output_type": "execute_result"
1726 |     }
1727 |    ],
1728 |    "source": [
1729 |     "out.casTable.fetch(to=5)"
1730 |    ]
1731 |   },
1732 |   {
1733 |    "cell_type": "code",
1734 |    "execution_count": 60,
1735 |    "metadata": {
1736 |     "collapsed": false
1737 |    },
1738 |    "outputs": [
1739 |     {
1740 |      "data": {
1741 |       "text/html": [
1742 |        "<div class=\"cas-results-key\"><b>&#167; Summary</b></div>\n",
1743 |        "<div class=\"cas-results-body\">\n",
1744 |        "<div>\n",
1745 |        "<table border=\"1\" class=\"dataframe\"><caption>Descriptive Statistics for DATA.IRIS</caption>\n",
1746 |        "  <thead>\n",
1747 |        "    <tr style=\"text-align: right;\">\n",
1748 |        "      <th title=\"\"></th>\n",
1749 |        "      <th title=\"Analysis Variable\">Column</th>\n",
1750 |        "      <th title=\"Minimum\">Min</th>\n",
1751 |        "      <th title=\"Maximum\">Max</th>\n",
1752 |        "      <th title=\"N\">N</th>\n",
1753 |        "      <th title=\"N Miss\">NMiss</th>\n",
1754 |        "      <th title=\"Mean\">Mean</th>\n",
1755 |        "      <th title=\"Sum\">Sum</th>\n",
1756 |        "      <th title=\"Std Dev\">Std</th>\n",
1757 |        "      <th title=\"Std Error\">StdErr</th>\n",
1758 |        "      <th title=\"Variance\">Var</th>\n",
1759 |        "      <th title=\"USS\">USS</th>\n",
1760 |        "      <th title=\"Corrected SS\">CSS</th>\n",
1761 |        "      <th title=\"Coeff of Variation\">CV</th>\n",
1762 |        "      <th title=\"t Value\">TValue</th>\n",
1763 |        "      <th title=\"Pr &gt; |t|\">ProbT</th>\n",
1764 |        "    </tr>\n",
1765 |        "  </thead>\n",
1766 |        "  <tbody>\n",
1767 |        "    <tr>\n",
1768 |        "      <th>0</th>\n",
1769 |        "      <td>sepal_length</td>\n",
1770 |        "      <td>4.3</td>\n",
1771 |        "      <td>7.9</td>\n",
1772 |        "      <td>150.0</td>\n",
1773 |        "      <td>0.0</td>\n",
1774 |        "      <td>5.843333</td>\n",
1775 |        "      <td>876.5</td>\n",
1776 |        "      <td>0.828066</td>\n",
1777 |        "      <td>0.067611</td>\n",
1778 |        "      <td>0.685694</td>\n",
1779 |        "      <td>5223.85</td>\n",
1780 |        "      <td>102.168333</td>\n",
1781 |        "      <td>14.171126</td>\n",
1782 |        "      <td>86.425375</td>\n",
1783 |        "      <td>3.331256e-129</td>\n",
1784 |        "    </tr>\n",
1785 |        "    <tr>\n",
1786 |        "      <th>1</th>\n",
1787 |        "      <td>sepal_width</td>\n",
1788 |        "      <td>2.0</td>\n",
1789 |        "      <td>4.4</td>\n",
1790 |        "      <td>150.0</td>\n",
1791 |        "      <td>0.0</td>\n",
1792 |        "      <td>3.054000</td>\n",
1793 |        "      <td>458.1</td>\n",
1794 |        "      <td>0.433594</td>\n",
1795 |        "      <td>0.035403</td>\n",
1796 |        "      <td>0.188004</td>\n",
1797 |        "      <td>1427.05</td>\n",
1798 |        "      <td>28.012600</td>\n",
1799 |        "      <td>14.197587</td>\n",
1800 |        "      <td>86.264297</td>\n",
1801 |        "      <td>4.374977e-129</td>\n",
1802 |        "    </tr>\n",
1803 |        "    <tr>\n",
1804 |        "      <th>2</th>\n",
1805 |        "      <td>petal_length</td>\n",
1806 |        "      <td>1.0</td>\n",
1807 |        "      <td>6.9</td>\n",
1808 |        "      <td>150.0</td>\n",
1809 |        "      <td>0.0</td>\n",
1810 |        "      <td>3.758667</td>\n",
1811 |        "      <td>563.8</td>\n",
1812 |        "      <td>1.764420</td>\n",
1813 |        "      <td>0.144064</td>\n",
1814 |        "      <td>3.113179</td>\n",
1815 |        "      <td>2583.00</td>\n",
1816 |        "      <td>463.863733</td>\n",
1817 |        "      <td>46.942721</td>\n",
1818 |        "      <td>26.090198</td>\n",
1819 |        "      <td>1.994305e-57</td>\n",
1820 |        "    </tr>\n",
1821 |        "    <tr>\n",
1822 |        "      <th>3</th>\n",
1823 |        "      <td>petal_width</td>\n",
1824 |        "      <td>0.1</td>\n",
1825 |        "      <td>2.5</td>\n",
1826 |        "      <td>150.0</td>\n",
1827 |        "      <td>0.0</td>\n",
1828 |        "      <td>1.198667</td>\n",
1829 |        "      <td>179.8</td>\n",
1830 |        "      <td>0.763161</td>\n",
1831 |        "      <td>0.062312</td>\n",
1832 |        "      <td>0.582414</td>\n",
1833 |        "      <td>302.30</td>\n",
1834 |        "      <td>86.779733</td>\n",
1835 |        "      <td>63.667470</td>\n",
1836 |        "      <td>19.236588</td>\n",
1837 |        "      <td>3.209704e-42</td>\n",
1838 |        "    </tr>\n",
1839 |        "  </tbody>\n",
1840 |        "</table>\n",
1841 |        "</div>\n",
1842 |        "</div>\n",
1843 |        "<div class=\"cas-output-area\"></div>\n",
1844 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0249s</span> &#183; <span class=\"cas-user\">user 0.025s</span> &#183; <span class=\"cas-sys\">sys 0.005s</span> &#183; <span class=\"cas-memory\">mem 1.74MB</span></small></p>"
1845 |       ],
1846 |       "text/plain": [
1847 |        "[Summary]\n",
1848 |        "\n",
1849 |        " Descriptive Statistics for DATA.IRIS\n",
1850 |        " \n",
1851 |        "          Column  Min  Max      N  NMiss      Mean    Sum       Std    StdErr  \\\n",
1852 |        " 0  sepal_length  4.3  7.9  150.0    0.0  5.843333  876.5  0.828066  0.067611   \n",
1853 |        " 1   sepal_width  2.0  4.4  150.0    0.0  3.054000  458.1  0.433594  0.035403   \n",
1854 |        " 2  petal_length  1.0  6.9  150.0    0.0  3.758667  563.8  1.764420  0.144064   \n",
1855 |        " 3   petal_width  0.1  2.5  150.0    0.0  1.198667  179.8  0.763161  0.062312   \n",
1856 |        " \n",
1857 |        "         Var      USS         CSS         CV     TValue          ProbT  \n",
1858 |        " 0  0.685694  5223.85  102.168333  14.171126  86.425375  3.331256e-129  \n",
1859 |        " 1  0.188004  1427.05   28.012600  14.197587  86.264297  4.374977e-129  \n",
1860 |        " 2  3.113179  2583.00  463.863733  46.942721  26.090198   1.994305e-57  \n",
1861 |        " 3  0.582414   302.30   86.779733  63.667470  19.236588   3.209704e-42  \n",
1862 |        "\n",
1863 |        "+ Elapsed: 0.0249s, user: 0.025s, sys: 0.005s, mem: 1.74mb"
1864 |       ]
1865 |      },
1866 |      "execution_count": 60,
1867 |      "metadata": {},
1868 |      "output_type": "execute_result"
1869 |     }
1870 |    ],
1871 |    "source": [
1872 |     "out.casTable.summary()"
1873 |    ]
1874 |   },
1875 |   {
1876 |    "cell_type": "code",
1877 |    "execution_count": 61,
1878 |    "metadata": {
1879 |     "collapsed": false
1880 |    },
1881 |    "outputs": [
1882 |     {
1883 |      "data": {
1884 |       "text/html": [
1885 |        "<div class=\"cas-results-key\"><b>&#167; CorrSimple</b></div>\n",
1886 |        "<div class=\"cas-results-body\">\n",
1887 |        "<div>\n",
1888 |        "<table border=\"1\" class=\"dataframe\"><caption>Summary Statistics in Correlation Analysis for DATA.IRIS</caption>\n",
1889 |        "  <thead>\n",
1890 |        "    <tr style=\"text-align: right;\">\n",
1891 |        "      <th title=\"\"></th>\n",
1892 |        "      <th title=\"Analysis Variable\">Variable</th>\n",
1893 |        "      <th title=\"N\">N</th>\n",
1894 |        "      <th title=\"Mean\">Mean</th>\n",
1895 |        "      <th title=\"Sum\">Sum</th>\n",
1896 |        "      <th title=\"Std Dev.\">StdDev</th>\n",
1897 |        "      <th title=\"Min\">Minimum</th>\n",
1898 |        "      <th title=\"Max\">Maximum</th>\n",
1899 |        "    </tr>\n",
1900 |        "  </thead>\n",
1901 |        "  <tbody>\n",
1902 |        "    <tr>\n",
1903 |        "      <th>0</th>\n",
1904 |        "      <td>sepal_length</td>\n",
1905 |        "      <td>150.0</td>\n",
1906 |        "      <td>5.843333</td>\n",
1907 |        "      <td>876.5</td>\n",
1908 |        "      <td>0.828066</td>\n",
1909 |        "      <td>4.3</td>\n",
1910 |        "      <td>7.9</td>\n",
1911 |        "    </tr>\n",
1912 |        "    <tr>\n",
1913 |        "      <th>1</th>\n",
1914 |        "      <td>sepal_width</td>\n",
1915 |        "      <td>150.0</td>\n",
1916 |        "      <td>3.054000</td>\n",
1917 |        "      <td>458.1</td>\n",
1918 |        "      <td>0.433594</td>\n",
1919 |        "      <td>2.0</td>\n",
1920 |        "      <td>4.4</td>\n",
1921 |        "    </tr>\n",
1922 |        "    <tr>\n",
1923 |        "      <th>2</th>\n",
1924 |        "      <td>petal_length</td>\n",
1925 |        "      <td>150.0</td>\n",
1926 |        "      <td>3.758667</td>\n",
1927 |        "      <td>563.8</td>\n",
1928 |        "      <td>1.764420</td>\n",
1929 |        "      <td>1.0</td>\n",
1930 |        "      <td>6.9</td>\n",
1931 |        "    </tr>\n",
1932 |        "    <tr>\n",
1933 |        "      <th>3</th>\n",
1934 |        "      <td>petal_width</td>\n",
1935 |        "      <td>150.0</td>\n",
1936 |        "      <td>1.198667</td>\n",
1937 |        "      <td>179.8</td>\n",
1938 |        "      <td>0.763161</td>\n",
1939 |        "      <td>0.1</td>\n",
1940 |        "      <td>2.5</td>\n",
1941 |        "    </tr>\n",
1942 |        "  </tbody>\n",
1943 |        "</table>\n",
1944 |        "</div>\n",
1945 |        "</div>\n",
1946 |        "<div class=\"cas-results-key\"><hr/><b>&#167; Correlation</b></div>\n",
1947 |        "<div class=\"cas-results-body\">\n",
1948 |        "<div>\n",
1949 |        "<table border=\"1\" class=\"dataframe\"><caption>Pearson Correlation Coefficients for DATA.IRIS</caption>\n",
1950 |        "  <thead>\n",
1951 |        "    <tr style=\"text-align: right;\">\n",
1952 |        "      <th title=\"\"></th>\n",
1953 |        "      <th title=\"Analysis Variable\">Variable</th>\n",
1954 |        "      <th title=\"sepal_length\">sepal_length</th>\n",
1955 |        "      <th title=\"sepal_width\">sepal_width</th>\n",
1956 |        "      <th title=\"petal_length\">petal_length</th>\n",
1957 |        "      <th title=\"petal_width\">petal_width</th>\n",
1958 |        "    </tr>\n",
1959 |        "  </thead>\n",
1960 |        "  <tbody>\n",
1961 |        "    <tr>\n",
1962 |        "      <th>0</th>\n",
1963 |        "      <td>sepal_length</td>\n",
1964 |        "      <td>1.000000</td>\n",
1965 |        "      <td>-0.109369</td>\n",
1966 |        "      <td>0.871754</td>\n",
1967 |        "      <td>0.817954</td>\n",
1968 |        "    </tr>\n",
1969 |        "    <tr>\n",
1970 |        "      <th>1</th>\n",
1971 |        "      <td>sepal_width</td>\n",
1972 |        "      <td>-0.109369</td>\n",
1973 |        "      <td>1.000000</td>\n",
1974 |        "      <td>-0.420516</td>\n",
1975 |        "      <td>-0.356544</td>\n",
1976 |        "    </tr>\n",
1977 |        "    <tr>\n",
1978 |        "      <th>2</th>\n",
1979 |        "      <td>petal_length</td>\n",
1980 |        "      <td>0.871754</td>\n",
1981 |        "      <td>-0.420516</td>\n",
1982 |        "      <td>1.000000</td>\n",
1983 |        "      <td>0.962757</td>\n",
1984 |        "    </tr>\n",
1985 |        "    <tr>\n",
1986 |        "      <th>3</th>\n",
1987 |        "      <td>petal_width</td>\n",
1988 |        "      <td>0.817954</td>\n",
1989 |        "      <td>-0.356544</td>\n",
1990 |        "      <td>0.962757</td>\n",
1991 |        "      <td>1.000000</td>\n",
1992 |        "    </tr>\n",
1993 |        "  </tbody>\n",
1994 |        "</table>\n",
1995 |        "</div>\n",
1996 |        "</div>\n",
1997 |        "<div class=\"cas-output-area\"></div>\n",
1998 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00596s</span> &#183; <span class=\"cas-user\">user 0.005s</span> &#183; <span class=\"cas-sys\">sys 0.004s</span> &#183; <span class=\"cas-memory\">mem 1.73MB</span></small></p>"
1999 |       ],
2000 |       "text/plain": [
2001 |        "[CorrSimple]\n",
2002 |        "\n",
2003 |        " Summary Statistics in Correlation Analysis for DATA.IRIS\n",
2004 |        " \n",
2005 |        "        Variable      N      Mean    Sum    StdDev  Minimum  Maximum\n",
2006 |        " 0  sepal_length  150.0  5.843333  876.5  0.828066      4.3      7.9\n",
2007 |        " 1   sepal_width  150.0  3.054000  458.1  0.433594      2.0      4.4\n",
2008 |        " 2  petal_length  150.0  3.758667  563.8  1.764420      1.0      6.9\n",
2009 |        " 3   petal_width  150.0  1.198667  179.8  0.763161      0.1      2.5\n",
2010 |        "\n",
2011 |        "[Correlation]\n",
2012 |        "\n",
2013 |        " Pearson Correlation Coefficients for DATA.IRIS\n",
2014 |        " \n",
2015 |        "        Variable  sepal_length  sepal_width  petal_length  petal_width\n",
2016 |        " 0  sepal_length      1.000000    -0.109369      0.871754     0.817954\n",
2017 |        " 1   sepal_width     -0.109369     1.000000     -0.420516    -0.356544\n",
2018 |        " 2  petal_length      0.871754    -0.420516      1.000000     0.962757\n",
2019 |        " 3   petal_width      0.817954    -0.356544      0.962757     1.000000\n",
2020 |        "\n",
2021 |        "+ Elapsed: 0.00596s, user: 0.005s, sys: 0.004s, mem: 1.73mb"
2022 |       ]
2023 |      },
2024 |      "execution_count": 61,
2025 |      "metadata": {},
2026 |      "output_type": "execute_result"
2027 |     }
2028 |    ],
2029 |    "source": [
2030 |     "out.casTable.correlation()"
2031 |    ]
2032 |   },
2033 |   {
2034 |    "cell_type": "code",
2035 |    "execution_count": 62,
2036 |    "metadata": {
2037 |     "collapsed": false
2038 |    },
2039 |    "outputs": [
2040 |     {
2041 |      "data": {
2042 |       "text/html": [
2043 |        "<div class=\"cas-results-key\"><b>&#167; userInfo</b></div>\n",
2044 |        "<div class=\"cas-results-body\">\n",
2045 |        "<div>{'anonymous': False, 'providedName': 'username', 'hostAccount': True, 'userId': 'username', 'groups': ['users'], 'providerName': 'Active Directory', 'uniqueId': 'username'}</div>\n",
2046 |        "</div>\n",
2047 |        "<div class=\"cas-output-area\"></div>\n",
2048 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.000302s</span> &#183; <span class=\"cas-memory\">mem 0.0656MB</span></small></p>"
2049 |       ],
2050 |       "text/plain": [
2051 |        "[userInfo]\n",
2052 |        "\n",
2053 |        " {'anonymous': False,\n",
2054 |        "  'groups': ['users'],\n",
2055 |        "  'hostAccount': True,\n",
2056 |        "  'providedName': 'username',\n",
2057 |        "  'providerName': 'Active Directory',\n",
2058 |        "  'uniqueId': 'username',\n",
2059 |        "  'userId': 'username'}\n",
2060 |        "\n",
2061 |        "+ Elapsed: 0.000302s, mem: 0.0656mb"
2062 |       ]
2063 |      },
2064 |      "execution_count": 62,
2065 |      "metadata": {},
2066 |      "output_type": "execute_result"
2067 |     }
2068 |    ],
2069 |    "source": [
2070 |     "out.casTable.userinfo()"
2071 |    ]
2072 |   },
2073 |   {
2074 |    "cell_type": "markdown",
2075 |    "metadata": {},
2076 |    "source": [
2077 |     "Even some Pandas DataFrame methods and attributes work on **CASTable** objects.  Here is a small sample."
2078 |    ]
2079 |   },
2080 |   {
2081 |    "cell_type": "code",
2082 |    "execution_count": 63,
2083 |    "metadata": {
2084 |     "collapsed": false
2085 |    },
2086 |    "outputs": [
2087 |     {
2088 |      "data": {
2089 |       "text/plain": [
2090 |        "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
2091 |        "       'species'],\n",
2092 |        "      dtype='object')"
2093 |       ]
2094 |      },
2095 |      "execution_count": 63,
2096 |      "metadata": {},
2097 |      "output_type": "execute_result"
2098 |     }
2099 |    ],
2100 |    "source": [
2101 |     "out.casTable.columns"
2102 |    ]
2103 |   },
2104 |   {
2105 |    "cell_type": "code",
2106 |    "execution_count": 64,
2107 |    "metadata": {
2108 |     "collapsed": false
2109 |    },
2110 |    "outputs": [
2111 |     {
2112 |      "data": {
2113 |       "text/plain": [
2114 |        "sepal_length     double\n",
2115 |        "sepal_width      double\n",
2116 |        "petal_length     double\n",
2117 |        "petal_width      double\n",
2118 |        "species         varchar\n",
2119 |        "dtype: object"
2120 |       ]
2121 |      },
2122 |      "execution_count": 64,
2123 |      "metadata": {},
2124 |      "output_type": "execute_result"
2125 |     }
2126 |    ],
2127 |    "source": [
2128 |     "out.casTable.dtypes"
2129 |    ]
2130 |   },
2131 |   {
2132 |    "cell_type": "code",
2133 |    "execution_count": 65,
2134 |    "metadata": {
2135 |     "collapsed": false
2136 |    },
2137 |    "outputs": [
2138 |     {
2139 |      "name": "stdout",
2140 |      "output_type": "stream",
2141 |      "text": [
2142 |       "CASTable('DATA.IRIS', caslib='CASUSER(username)')\n",
2143 |       "Data columns (total 5 columns):\n",
2144 |       "                N   Miss     Type\n",
2145 |       "sepal_length  150  False   double\n",
2146 |       "sepal_width   150  False   double\n",
2147 |       "petal_length  150  False   double\n",
2148 |       "petal_width   150  False   double\n",
2149 |       "species       150  False  varchar\n",
2150 |       "dtypes: double(4), varchar(1)\n",
2151 |       "data size: 8450\n",
2152 |       "vardata size: 1250\n",
2153 |       "memory usage: 8536\n"
2154 |      ]
2155 |     }
2156 |    ],
2157 |    "source": [
2158 |     "out.casTable.info()"
2159 |    ]
2160 |   },
2161 |   {
2162 |    "cell_type": "code",
2163 |    "execution_count": 67,
2164 |    "metadata": {
2165 |     "collapsed": false
2166 |    },
2167 |    "outputs": [
2168 |     {
2169 |      "data": {
2170 |       "text/html": [
2171 |        "<div>\n",
2172 |        "<table border=\"1\" class=\"dataframe\">\n",
2173 |        "  <thead>\n",
2174 |        "    <tr style=\"text-align: right;\">\n",
2175 |        "      <th></th>\n",
2176 |        "      <th>sepal_length</th>\n",
2177 |        "      <th>sepal_width</th>\n",
2178 |        "      <th>petal_length</th>\n",
2179 |        "      <th>petal_width</th>\n",
2180 |        "      <th>species</th>\n",
2181 |        "    </tr>\n",
2182 |        "  </thead>\n",
2183 |        "  <tbody>\n",
2184 |        "    <tr>\n",
2185 |        "      <th>count</th>\n",
2186 |        "      <td>150</td>\n",
2187 |        "      <td>150</td>\n",
2188 |        "      <td>150</td>\n",
2189 |        "      <td>150</td>\n",
2190 |        "      <td>150</td>\n",
2191 |        "    </tr>\n",
2192 |        "    <tr>\n",
2193 |        "      <th>unique</th>\n",
2194 |        "      <td>35</td>\n",
2195 |        "      <td>23</td>\n",
2196 |        "      <td>43</td>\n",
2197 |        "      <td>22</td>\n",
2198 |        "      <td>3</td>\n",
2199 |        "    </tr>\n",
2200 |        "    <tr>\n",
2201 |        "      <th>top</th>\n",
2202 |        "      <td>5</td>\n",
2203 |        "      <td>3</td>\n",
2204 |        "      <td>1.5</td>\n",
2205 |        "      <td>0.2</td>\n",
2206 |        "      <td>virginica</td>\n",
2207 |        "    </tr>\n",
2208 |        "    <tr>\n",
2209 |        "      <th>freq</th>\n",
2210 |        "      <td>10</td>\n",
2211 |        "      <td>26</td>\n",
2212 |        "      <td>14</td>\n",
2213 |        "      <td>28</td>\n",
2214 |        "      <td>50</td>\n",
2215 |        "    </tr>\n",
2216 |        "    <tr>\n",
2217 |        "      <th>mean</th>\n",
2218 |        "      <td>5.84333</td>\n",
2219 |        "      <td>3.054</td>\n",
2220 |        "      <td>3.75867</td>\n",
2221 |        "      <td>1.19867</td>\n",
2222 |        "      <td>NaN</td>\n",
2223 |        "    </tr>\n",
2224 |        "    <tr>\n",
2225 |        "      <th>std</th>\n",
2226 |        "      <td>0.828066</td>\n",
2227 |        "      <td>0.433594</td>\n",
2228 |        "      <td>1.76442</td>\n",
2229 |        "      <td>0.763161</td>\n",
2230 |        "      <td>NaN</td>\n",
2231 |        "    </tr>\n",
2232 |        "    <tr>\n",
2233 |        "      <th>min</th>\n",
2234 |        "      <td>4.3</td>\n",
2235 |        "      <td>2</td>\n",
2236 |        "      <td>1</td>\n",
2237 |        "      <td>0.1</td>\n",
2238 |        "      <td>setosa</td>\n",
2239 |        "    </tr>\n",
2240 |        "    <tr>\n",
2241 |        "      <th>40%</th>\n",
2242 |        "      <td>5.6</td>\n",
2243 |        "      <td>3</td>\n",
2244 |        "      <td>3.9</td>\n",
2245 |        "      <td>1.15</td>\n",
2246 |        "      <td>NaN</td>\n",
2247 |        "    </tr>\n",
2248 |        "    <tr>\n",
2249 |        "      <th>50%</th>\n",
2250 |        "      <td>5.8</td>\n",
2251 |        "      <td>3</td>\n",
2252 |        "      <td>4.35</td>\n",
2253 |        "      <td>1.3</td>\n",
2254 |        "      <td>NaN</td>\n",
2255 |        "    </tr>\n",
2256 |        "    <tr>\n",
2257 |        "      <th>80%</th>\n",
2258 |        "      <td>6.55</td>\n",
2259 |        "      <td>3.4</td>\n",
2260 |        "      <td>5.35</td>\n",
2261 |        "      <td>1.9</td>\n",
2262 |        "      <td>NaN</td>\n",
2263 |        "    </tr>\n",
2264 |        "    <tr>\n",
2265 |        "      <th>max</th>\n",
2266 |        "      <td>7.9</td>\n",
2267 |        "      <td>4.4</td>\n",
2268 |        "      <td>6.9</td>\n",
2269 |        "      <td>2.5</td>\n",
2270 |        "      <td>virginica</td>\n",
2271 |        "    </tr>\n",
2272 |        "  </tbody>\n",
2273 |        "</table>\n",
2274 |        "</div>"
2275 |       ],
2276 |       "text/plain": [
2277 |        "       sepal_length sepal_width petal_length petal_width    species\n",
2278 |        "count           150         150          150         150        150\n",
2279 |        "unique           35          23           43          22          3\n",
2280 |        "top               5           3          1.5         0.2  virginica\n",
2281 |        "freq             10          26           14          28         50\n",
2282 |        "mean        5.84333       3.054      3.75867     1.19867        NaN\n",
2283 |        "std        0.828066    0.433594      1.76442    0.763161        NaN\n",
2284 |        "min             4.3           2            1         0.1     setosa\n",
2285 |        "40%             5.6           3          3.9        1.15        NaN\n",
2286 |        "50%             5.8           3         4.35         1.3        NaN\n",
2287 |        "80%            6.55         3.4         5.35         1.9        NaN\n",
2288 |        "max             7.9         4.4          6.9         2.5  virginica"
2289 |       ]
2290 |      },
2291 |      "execution_count": 67,
2292 |      "metadata": {},
2293 |      "output_type": "execute_result"
2294 |     }
2295 |    ],
2296 |    "source": [
2297 |     "out.casTable.describe(include=['all'], percentiles=[.4, .8])"
2298 |    ]
2299 |   },
2300 |   {
2301 |    "cell_type": "markdown",
2302 |    "metadata": {},
2303 |    "source": [
2304 |     "### Manually Creating a CASTable Object"
2305 |    ]
2306 |   },
2307 |   {
2308 |    "cell_type": "code",
2309 |    "execution_count": 68,
2310 |    "metadata": {
2311 |     "collapsed": false
2312 |    },
2313 |    "outputs": [
2314 |     {
2315 |      "data": {
2316 |       "text/plain": [
2317 |        "CASTable('data.iris', caslib='casuser')"
2318 |       ]
2319 |      },
2320 |      "execution_count": 68,
2321 |      "metadata": {},
2322 |      "output_type": "execute_result"
2323 |     }
2324 |    ],
2325 |    "source": [
2326 |     "newtbl = conn.CASTable('data.iris', caslib='casuser')\n",
2327 |     "newtbl"
2328 |    ]
2329 |   },
2330 |   {
2331 |    "cell_type": "code",
2332 |    "execution_count": 69,
2333 |    "metadata": {
2334 |     "collapsed": false
2335 |    },
2336 |    "outputs": [
2337 |     {
2338 |      "data": {
2339 |       "text/html": [
2340 |        "<div class=\"cas-results-key\"><b>&#167; ColumnInfo</b></div>\n",
2341 |        "<div class=\"cas-results-body\">\n",
2342 |        "<div>\n",
2343 |        "<table border=\"1\" class=\"dataframe\">\n",
2344 |        "  <thead>\n",
2345 |        "    <tr style=\"text-align: right;\">\n",
2346 |        "      <th title=\"\"></th>\n",
2347 |        "      <th title=\"Column\">Column</th>\n",
2348 |        "      <th title=\"ID\">ID</th>\n",
2349 |        "      <th title=\"Type\">Type</th>\n",
2350 |        "      <th title=\"RawLength\">RawLength</th>\n",
2351 |        "      <th title=\"FormattedLength\">FormattedLength</th>\n",
2352 |        "      <th title=\"NFL\">NFL</th>\n",
2353 |        "      <th title=\"NFD\">NFD</th>\n",
2354 |        "    </tr>\n",
2355 |        "  </thead>\n",
2356 |        "  <tbody>\n",
2357 |        "    <tr>\n",
2358 |        "      <th>0</th>\n",
2359 |        "      <td>sepal_length</td>\n",
2360 |        "      <td>1</td>\n",
2361 |        "      <td>double</td>\n",
2362 |        "      <td>8</td>\n",
2363 |        "      <td>12</td>\n",
2364 |        "      <td>0</td>\n",
2365 |        "      <td>0</td>\n",
2366 |        "    </tr>\n",
2367 |        "    <tr>\n",
2368 |        "      <th>1</th>\n",
2369 |        "      <td>sepal_width</td>\n",
2370 |        "      <td>2</td>\n",
2371 |        "      <td>double</td>\n",
2372 |        "      <td>8</td>\n",
2373 |        "      <td>12</td>\n",
2374 |        "      <td>0</td>\n",
2375 |        "      <td>0</td>\n",
2376 |        "    </tr>\n",
2377 |        "    <tr>\n",
2378 |        "      <th>2</th>\n",
2379 |        "      <td>petal_length</td>\n",
2380 |        "      <td>3</td>\n",
2381 |        "      <td>double</td>\n",
2382 |        "      <td>8</td>\n",
2383 |        "      <td>12</td>\n",
2384 |        "      <td>0</td>\n",
2385 |        "      <td>0</td>\n",
2386 |        "    </tr>\n",
2387 |        "    <tr>\n",
2388 |        "      <th>3</th>\n",
2389 |        "      <td>petal_width</td>\n",
2390 |        "      <td>4</td>\n",
2391 |        "      <td>double</td>\n",
2392 |        "      <td>8</td>\n",
2393 |        "      <td>12</td>\n",
2394 |        "      <td>0</td>\n",
2395 |        "      <td>0</td>\n",
2396 |        "    </tr>\n",
2397 |        "    <tr>\n",
2398 |        "      <th>4</th>\n",
2399 |        "      <td>species</td>\n",
2400 |        "      <td>5</td>\n",
2401 |        "      <td>varchar</td>\n",
2402 |        "      <td>10</td>\n",
2403 |        "      <td>10</td>\n",
2404 |        "      <td>0</td>\n",
2405 |        "      <td>0</td>\n",
2406 |        "    </tr>\n",
2407 |        "  </tbody>\n",
2408 |        "</table>\n",
2409 |        "</div>\n",
2410 |        "</div>\n",
2411 |        "<div class=\"cas-output-area\"></div>\n",
2412 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.000687s</span> &#183; <span class=\"cas-user\">user 0.001s</span> &#183; <span class=\"cas-memory\">mem 0.169MB</span></small></p>"
2413 |       ],
2414 |       "text/plain": [
2415 |        "[ColumnInfo]\n",
2416 |        "\n",
2417 |        "          Column  ID     Type  RawLength  FormattedLength  NFL  NFD\n",
2418 |        " 0  sepal_length   1   double          8               12    0    0\n",
2419 |        " 1   sepal_width   2   double          8               12    0    0\n",
2420 |        " 2  petal_length   3   double          8               12    0    0\n",
2421 |        " 3   petal_width   4   double          8               12    0    0\n",
2422 |        " 4       species   5  varchar         10               10    0    0\n",
2423 |        "\n",
2424 |        "+ Elapsed: 0.000687s, user: 0.001s, mem: 0.169mb"
2425 |       ]
2426 |      },
2427 |      "execution_count": 69,
2428 |      "metadata": {},
2429 |      "output_type": "execute_result"
2430 |     }
2431 |    ],
2432 |    "source": [
2433 |     "newtbl.columninfo()"
2434 |    ]
2435 |   },
2436 |   {
2437 |    "cell_type": "markdown",
2438 |    "metadata": {},
2439 |    "source": [
2440 |     "### The CASTable Action Interface"
2441 |    ]
2442 |   },
2443 |   {
2444 |    "cell_type": "code",
2445 |    "execution_count": 70,
2446 |    "metadata": {
2447 |     "collapsed": false
2448 |    },
2449 |    "outputs": [
2450 |     {
2451 |      "name": "stderr",
2452 |      "output_type": "stream",
2453 |      "text": [
2454 |       "ERROR: The table DATA.IRIS already exists in caslib CASUSER(username).\n",
2455 |       "ERROR: The action stopped due to errors.\n"
2456 |      ]
2457 |     },
2458 |     {
2459 |      "data": {
2460 |       "text/html": [
2461 |        "<div class=\"cas-results-key\"><b>&#167; caslib</b></div>\n",
2462 |        "<div class=\"cas-results-body\">\n",
2463 |        "<div>CASUSER(username)</div>\n",
2464 |        "</div>\n",
2465 |        "<div class=\"cas-results-key\"><hr/><b>&#167; tableName</b></div>\n",
2466 |        "<div class=\"cas-results-body\">\n",
2467 |        "<div>DATA.IRIS</div>\n",
2468 |        "</div>\n",
2469 |        "<div class=\"cas-results-key\"><hr/><b>&#167; casTable</b></div>\n",
2470 |        "<div class=\"cas-results-body\">\n",
2471 |        "<div>CASTable('DATA.IRIS', caslib='CASUSER(username)')</div>\n",
2472 |        "</div>\n",
2473 |        "<div class=\"cas-output-area\"></div>\n",
2474 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.000518s</span> &#183; <span class=\"cas-memory\">mem 0.123MB</span></small></p>"
2475 |       ],
2476 |       "text/plain": [
2477 |        "[caslib]\n",
2478 |        "\n",
2479 |        " 'CASUSER(username)'\n",
2480 |        "\n",
2481 |        "[tableName]\n",
2482 |        "\n",
2483 |        " 'DATA.IRIS'\n",
2484 |        "\n",
2485 |        "[casTable]\n",
2486 |        "\n",
2487 |        " CASTable('DATA.IRIS', caslib='CASUSER(username)')\n",
2488 |        "\n",
2489 |        "+ Elapsed: 0.000518s, mem: 0.123mb"
2490 |       ]
2491 |      },
2492 |      "execution_count": 70,
2493 |      "metadata": {},
2494 |      "output_type": "execute_result"
2495 |     }
2496 |    ],
2497 |    "source": [
2498 |     "out = conn.loadtable('data/iris.csv', caslib='casuser')\n",
2499 |     "out"
2500 |    ]
2501 |   },
2502 |   {
2503 |    "cell_type": "code",
2504 |    "execution_count": 71,
2505 |    "metadata": {
2506 |     "collapsed": true
2507 |    },
2508 |    "outputs": [],
2509 |    "source": [
2510 |     "iris = out.casTable"
2511 |    ]
2512 |   },
2513 |   {
2514 |    "cell_type": "code",
2515 |    "execution_count": 74,
2516 |    "metadata": {
2517 |     "collapsed": false
2518 |    },
2519 |    "outputs": [
2520 |     {
2521 |      "data": {
2522 |       "text/plain": [
2523 |        "?.simple.Summary(__table__=CASTable('DATA.IRIS', caslib='CASUSER(username)'))"
2524 |       ]
2525 |      },
2526 |      "execution_count": 74,
2527 |      "metadata": {},
2528 |      "output_type": "execute_result"
2529 |     }
2530 |    ],
2531 |    "source": [
2532 |     "summ = iris.Summary()\n",
2533 |     "summ"
2534 |    ]
2535 |   },
2536 |   {
2537 |    "cell_type": "code",
2538 |    "execution_count": 75,
2539 |    "metadata": {
2540 |     "collapsed": false
2541 |    },
2542 |    "outputs": [
2543 |     {
2544 |      "data": {
2545 |       "text/html": [
2546 |        "<div class=\"cas-results-key\"><b>&#167; Summary</b></div>\n",
2547 |        "<div class=\"cas-results-body\">\n",
2548 |        "<div>\n",
2549 |        "<table border=\"1\" class=\"dataframe\"><caption>Descriptive Statistics for DATA.IRIS</caption>\n",
2550 |        "  <thead>\n",
2551 |        "    <tr style=\"text-align: right;\">\n",
2552 |        "      <th title=\"\"></th>\n",
2553 |        "      <th title=\"Analysis Variable\">Column</th>\n",
2554 |        "      <th title=\"Minimum\">Min</th>\n",
2555 |        "      <th title=\"Maximum\">Max</th>\n",
2556 |        "      <th title=\"N\">N</th>\n",
2557 |        "      <th title=\"N Miss\">NMiss</th>\n",
2558 |        "      <th title=\"Mean\">Mean</th>\n",
2559 |        "      <th title=\"Sum\">Sum</th>\n",
2560 |        "      <th title=\"Std Dev\">Std</th>\n",
2561 |        "      <th title=\"Std Error\">StdErr</th>\n",
2562 |        "      <th title=\"Variance\">Var</th>\n",
2563 |        "      <th title=\"USS\">USS</th>\n",
2564 |        "      <th title=\"Corrected SS\">CSS</th>\n",
2565 |        "      <th title=\"Coeff of Variation\">CV</th>\n",
2566 |        "      <th title=\"t Value\">TValue</th>\n",
2567 |        "      <th title=\"Pr &gt; |t|\">ProbT</th>\n",
2568 |        "    </tr>\n",
2569 |        "  </thead>\n",
2570 |        "  <tbody>\n",
2571 |        "    <tr>\n",
2572 |        "      <th>0</th>\n",
2573 |        "      <td>sepal_length</td>\n",
2574 |        "      <td>4.3</td>\n",
2575 |        "      <td>7.9</td>\n",
2576 |        "      <td>150.0</td>\n",
2577 |        "      <td>0.0</td>\n",
2578 |        "      <td>5.843333</td>\n",
2579 |        "      <td>876.5</td>\n",
2580 |        "      <td>0.828066</td>\n",
2581 |        "      <td>0.067611</td>\n",
2582 |        "      <td>0.685694</td>\n",
2583 |        "      <td>5223.85</td>\n",
2584 |        "      <td>102.168333</td>\n",
2585 |        "      <td>14.171126</td>\n",
2586 |        "      <td>86.425375</td>\n",
2587 |        "      <td>3.331256e-129</td>\n",
2588 |        "    </tr>\n",
2589 |        "    <tr>\n",
2590 |        "      <th>1</th>\n",
2591 |        "      <td>sepal_width</td>\n",
2592 |        "      <td>2.0</td>\n",
2593 |        "      <td>4.4</td>\n",
2594 |        "      <td>150.0</td>\n",
2595 |        "      <td>0.0</td>\n",
2596 |        "      <td>3.054000</td>\n",
2597 |        "      <td>458.1</td>\n",
2598 |        "      <td>0.433594</td>\n",
2599 |        "      <td>0.035403</td>\n",
2600 |        "      <td>0.188004</td>\n",
2601 |        "      <td>1427.05</td>\n",
2602 |        "      <td>28.012600</td>\n",
2603 |        "      <td>14.197587</td>\n",
2604 |        "      <td>86.264297</td>\n",
2605 |        "      <td>4.374977e-129</td>\n",
2606 |        "    </tr>\n",
2607 |        "    <tr>\n",
2608 |        "      <th>2</th>\n",
2609 |        "      <td>petal_length</td>\n",
2610 |        "      <td>1.0</td>\n",
2611 |        "      <td>6.9</td>\n",
2612 |        "      <td>150.0</td>\n",
2613 |        "      <td>0.0</td>\n",
2614 |        "      <td>3.758667</td>\n",
2615 |        "      <td>563.8</td>\n",
2616 |        "      <td>1.764420</td>\n",
2617 |        "      <td>0.144064</td>\n",
2618 |        "      <td>3.113179</td>\n",
2619 |        "      <td>2583.00</td>\n",
2620 |        "      <td>463.863733</td>\n",
2621 |        "      <td>46.942721</td>\n",
2622 |        "      <td>26.090198</td>\n",
2623 |        "      <td>1.994305e-57</td>\n",
2624 |        "    </tr>\n",
2625 |        "    <tr>\n",
2626 |        "      <th>3</th>\n",
2627 |        "      <td>petal_width</td>\n",
2628 |        "      <td>0.1</td>\n",
2629 |        "      <td>2.5</td>\n",
2630 |        "      <td>150.0</td>\n",
2631 |        "      <td>0.0</td>\n",
2632 |        "      <td>1.198667</td>\n",
2633 |        "      <td>179.8</td>\n",
2634 |        "      <td>0.763161</td>\n",
2635 |        "      <td>0.062312</td>\n",
2636 |        "      <td>0.582414</td>\n",
2637 |        "      <td>302.30</td>\n",
2638 |        "      <td>86.779733</td>\n",
2639 |        "      <td>63.667470</td>\n",
2640 |        "      <td>19.236588</td>\n",
2641 |        "      <td>3.209704e-42</td>\n",
2642 |        "    </tr>\n",
2643 |        "  </tbody>\n",
2644 |        "</table>\n",
2645 |        "</div>\n",
2646 |        "</div>\n",
2647 |        "<div class=\"cas-output-area\"></div>\n",
2648 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00621s</span> &#183; <span class=\"cas-user\">user 0.004s</span> &#183; <span class=\"cas-sys\">sys 0.005s</span> &#183; <span class=\"cas-memory\">mem 1.73MB</span></small></p>"
2649 |       ],
2650 |       "text/plain": [
2651 |        "[Summary]\n",
2652 |        "\n",
2653 |        " Descriptive Statistics for DATA.IRIS\n",
2654 |        " \n",
2655 |        "          Column  Min  Max      N  NMiss      Mean    Sum       Std    StdErr  \\\n",
2656 |        " 0  sepal_length  4.3  7.9  150.0    0.0  5.843333  876.5  0.828066  0.067611   \n",
2657 |        " 1   sepal_width  2.0  4.4  150.0    0.0  3.054000  458.1  0.433594  0.035403   \n",
2658 |        " 2  petal_length  1.0  6.9  150.0    0.0  3.758667  563.8  1.764420  0.144064   \n",
2659 |        " 3   petal_width  0.1  2.5  150.0    0.0  1.198667  179.8  0.763161  0.062312   \n",
2660 |        " \n",
2661 |        "         Var      USS         CSS         CV     TValue          ProbT  \n",
2662 |        " 0  0.685694  5223.85  102.168333  14.171126  86.425375  3.331256e-129  \n",
2663 |        " 1  0.188004  1427.05   28.012600  14.197587  86.264297  4.374977e-129  \n",
2664 |        " 2  3.113179  2583.00  463.863733  46.942721  26.090198   1.994305e-57  \n",
2665 |        " 3  0.582414   302.30   86.779733  63.667470  19.236588   3.209704e-42  \n",
2666 |        "\n",
2667 |        "+ Elapsed: 0.00621s, user: 0.004s, sys: 0.005s, mem: 1.73mb"
2668 |       ]
2669 |      },
2670 |      "execution_count": 75,
2671 |      "metadata": {},
2672 |      "output_type": "execute_result"
2673 |     }
2674 |    ],
2675 |    "source": [
2676 |     "summ()"
2677 |    ]
2678 |   },
2679 |   {
2680 |    "cell_type": "markdown",
2681 |    "metadata": {},
2682 |    "source": [
2683 |     "### Setting CASTable Parameters"
2684 |    ]
2685 |   },
2686 |   {
2687 |    "cell_type": "code",
2688 |    "execution_count": 76,
2689 |    "metadata": {
2690 |     "collapsed": true
2691 |    },
2692 |    "outputs": [],
2693 |    "source": [
2694 |     "iris?"
2695 |    ]
2696 |   },
2697 |   {
2698 |    "cell_type": "code",
2699 |    "execution_count": 77,
2700 |    "metadata": {
2701 |     "collapsed": false
2702 |    },
2703 |    "outputs": [
2704 |     {
2705 |      "data": {
2706 |       "text/plain": [
2707 |        "CASTable('data.iris', caslib='casuser', computedvars=['length_factor'], computedvarsprogram='length_factor =\\n                            sepal_length * petal_length;', where='sepal_length > 6.8 and \\n                              species = \"virginica\"')"
2708 |       ]
2709 |      },
2710 |      "execution_count": 77,
2711 |      "metadata": {},
2712 |      "output_type": "execute_result"
2713 |     }
2714 |    ],
2715 |    "source": [
2716 |     "iris = conn.CASTable('data.iris', caslib='casuser', \n",
2717 |     "                     where='''sepal_length > 6.8 and \n",
2718 |     "                              species = \"virginica\"''',\n",
2719 |     "                     computedvars=['length_factor'],\n",
2720 |     "                     computedvarsprogram='''length_factor =\n",
2721 |     "                            sepal_length * petal_length;''')\n",
2722 |     "iris"
2723 |    ]
2724 |   },
2725 |   {
2726 |    "cell_type": "code",
2727 |    "execution_count": 78,
2728 |    "metadata": {
2729 |     "collapsed": false,
2730 |     "scrolled": true
2731 |    },
2732 |    "outputs": [
2733 |     {
2734 |      "data": {
2735 |       "text/html": [
2736 |        "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
2737 |        "<div class=\"cas-results-body\">\n",
2738 |        "<div>\n",
2739 |        "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table DATA.IRIS</caption>\n",
2740 |        "  <thead>\n",
2741 |        "    <tr style=\"text-align: right;\">\n",
2742 |        "      <th title=\"\"></th>\n",
2743 |        "      <th title=\"sepal_length\">sepal_length</th>\n",
2744 |        "      <th title=\"petal_length\">petal_length</th>\n",
2745 |        "      <th title=\"length_factor\">length_factor</th>\n",
2746 |        "    </tr>\n",
2747 |        "  </thead>\n",
2748 |        "  <tbody>\n",
2749 |        "    <tr>\n",
2750 |        "      <th>0</th>\n",
2751 |        "      <td>6.9</td>\n",
2752 |        "      <td>5.4</td>\n",
2753 |        "      <td>37.26</td>\n",
2754 |        "    </tr>\n",
2755 |        "    <tr>\n",
2756 |        "      <th>1</th>\n",
2757 |        "      <td>6.9</td>\n",
2758 |        "      <td>5.1</td>\n",
2759 |        "      <td>35.19</td>\n",
2760 |        "    </tr>\n",
2761 |        "    <tr>\n",
2762 |        "      <th>2</th>\n",
2763 |        "      <td>7.1</td>\n",
2764 |        "      <td>5.9</td>\n",
2765 |        "      <td>41.89</td>\n",
2766 |        "    </tr>\n",
2767 |        "    <tr>\n",
2768 |        "      <th>3</th>\n",
2769 |        "      <td>7.6</td>\n",
2770 |        "      <td>6.6</td>\n",
2771 |        "      <td>50.16</td>\n",
2772 |        "    </tr>\n",
2773 |        "    <tr>\n",
2774 |        "      <th>4</th>\n",
2775 |        "      <td>7.3</td>\n",
2776 |        "      <td>6.3</td>\n",
2777 |        "      <td>45.99</td>\n",
2778 |        "    </tr>\n",
2779 |        "    <tr>\n",
2780 |        "      <th>5</th>\n",
2781 |        "      <td>7.2</td>\n",
2782 |        "      <td>6.1</td>\n",
2783 |        "      <td>43.92</td>\n",
2784 |        "    </tr>\n",
2785 |        "    <tr>\n",
2786 |        "      <th>6</th>\n",
2787 |        "      <td>7.7</td>\n",
2788 |        "      <td>6.7</td>\n",
2789 |        "      <td>51.59</td>\n",
2790 |        "    </tr>\n",
2791 |        "    <tr>\n",
2792 |        "      <th>7</th>\n",
2793 |        "      <td>7.7</td>\n",
2794 |        "      <td>6.9</td>\n",
2795 |        "      <td>53.13</td>\n",
2796 |        "    </tr>\n",
2797 |        "    <tr>\n",
2798 |        "      <th>8</th>\n",
2799 |        "      <td>6.9</td>\n",
2800 |        "      <td>5.7</td>\n",
2801 |        "      <td>39.33</td>\n",
2802 |        "    </tr>\n",
2803 |        "    <tr>\n",
2804 |        "      <th>9</th>\n",
2805 |        "      <td>7.7</td>\n",
2806 |        "      <td>6.7</td>\n",
2807 |        "      <td>51.59</td>\n",
2808 |        "    </tr>\n",
2809 |        "    <tr>\n",
2810 |        "      <th>10</th>\n",
2811 |        "      <td>7.2</td>\n",
2812 |        "      <td>6.0</td>\n",
2813 |        "      <td>43.20</td>\n",
2814 |        "    </tr>\n",
2815 |        "    <tr>\n",
2816 |        "      <th>11</th>\n",
2817 |        "      <td>7.2</td>\n",
2818 |        "      <td>5.8</td>\n",
2819 |        "      <td>41.76</td>\n",
2820 |        "    </tr>\n",
2821 |        "    <tr>\n",
2822 |        "      <th>12</th>\n",
2823 |        "      <td>7.4</td>\n",
2824 |        "      <td>6.1</td>\n",
2825 |        "      <td>45.14</td>\n",
2826 |        "    </tr>\n",
2827 |        "    <tr>\n",
2828 |        "      <th>13</th>\n",
2829 |        "      <td>7.9</td>\n",
2830 |        "      <td>6.4</td>\n",
2831 |        "      <td>50.56</td>\n",
2832 |        "    </tr>\n",
2833 |        "    <tr>\n",
2834 |        "      <th>14</th>\n",
2835 |        "      <td>7.7</td>\n",
2836 |        "      <td>6.1</td>\n",
2837 |        "      <td>46.97</td>\n",
2838 |        "    </tr>\n",
2839 |        "  </tbody>\n",
2840 |        "</table>\n",
2841 |        "</div>\n",
2842 |        "</div>\n",
2843 |        "<div class=\"cas-output-area\"></div>\n",
2844 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0135s</span> &#183; <span class=\"cas-user\">user 0.013s</span> &#183; <span class=\"cas-sys\">sys 0.003s</span> &#183; <span class=\"cas-memory\">mem 2.54MB</span></small></p>"
2845 |       ],
2846 |       "text/plain": [
2847 |        "[Fetch]\n",
2848 |        "\n",
2849 |        " Selected Rows from Table DATA.IRIS\n",
2850 |        " \n",
2851 |        "     sepal_length  petal_length  length_factor\n",
2852 |        " 0            6.9           5.4          37.26\n",
2853 |        " 1            6.9           5.1          35.19\n",
2854 |        " 2            7.1           5.9          41.89\n",
2855 |        " 3            7.6           6.6          50.16\n",
2856 |        " 4            7.3           6.3          45.99\n",
2857 |        " 5            7.2           6.1          43.92\n",
2858 |        " 6            7.7           6.7          51.59\n",
2859 |        " 7            7.7           6.9          53.13\n",
2860 |        " 8            6.9           5.7          39.33\n",
2861 |        " 9            7.7           6.7          51.59\n",
2862 |        " 10           7.2           6.0          43.20\n",
2863 |        " 11           7.2           5.8          41.76\n",
2864 |        " 12           7.4           6.1          45.14\n",
2865 |        " 13           7.9           6.4          50.56\n",
2866 |        " 14           7.7           6.1          46.97\n",
2867 |        "\n",
2868 |        "+ Elapsed: 0.0135s, user: 0.013s, sys: 0.003s, mem: 2.54mb"
2869 |       ]
2870 |      },
2871 |      "execution_count": 78,
2872 |      "metadata": {},
2873 |      "output_type": "execute_result"
2874 |     }
2875 |    ],
2876 |    "source": [
2877 |     "iris.fetch(fetchvars=['sepal_length', 'petal_length', 'length_factor'])"
2878 |    ]
2879 |   },
2880 |   {
2881 |    "cell_type": "markdown",
2882 |    "metadata": {},
2883 |    "source": [
2884 |     "Using a **CASTable** object as a **casout=** parameter value."
2885 |    ]
2886 |   },
2887 |   {
2888 |    "cell_type": "raw",
2889 |    "metadata": {},
2890 |    "source": [
2891 |     "outtbl = conn.CASTable('summout', caslib='casuser', promote=True)\n",
2892 |     "outtbl"
2893 |    ]
2894 |   },
2895 |   {
2896 |    "cell_type": "code",
2897 |    "execution_count": 84,
2898 |    "metadata": {
2899 |     "collapsed": false
2900 |    },
2901 |    "outputs": [
2902 |     {
2903 |      "data": {
2904 |       "text/html": [
2905 |        "<div class=\"cas-results-key\"><b>&#167; OutputCasTables</b></div>\n",
2906 |        "<div class=\"cas-results-body\">\n",
2907 |        "<div>\n",
2908 |        "<table border=\"1\" class=\"dataframe\">\n",
2909 |        "  <thead>\n",
2910 |        "    <tr style=\"text-align: right;\">\n",
2911 |        "      <th title=\"\"></th>\n",
2912 |        "      <th title=\"CAS Library\">casLib</th>\n",
2913 |        "      <th title=\"Name\">Name</th>\n",
2914 |        "      <th title=\"Number of Rows\">Rows</th>\n",
2915 |        "      <th title=\"Number of Columns\">Columns</th>\n",
2916 |        "      <th title=\"Table\">casTable</th>\n",
2917 |        "    </tr>\n",
2918 |        "  </thead>\n",
2919 |        "  <tbody>\n",
2920 |        "    <tr>\n",
2921 |        "      <th>0</th>\n",
2922 |        "      <td>CASUSER(username)</td>\n",
2923 |        "      <td>summout</td>\n",
2924 |        "      <td>5</td>\n",
2925 |        "      <td>15</td>\n",
2926 |        "      <td>CASTable('summout', caslib='CASUSER(username)')</td>\n",
2927 |        "    </tr>\n",
2928 |        "  </tbody>\n",
2929 |        "</table>\n",
2930 |        "</div>\n",
2931 |        "</div>\n",
2932 |        "<div class=\"cas-output-area\"></div>\n",
2933 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0177s</span> &#183; <span class=\"cas-user\">user 0.016s</span> &#183; <span class=\"cas-sys\">sys 0.006s</span> &#183; <span class=\"cas-memory\">mem 2.62MB</span></small></p>"
2934 |       ],
2935 |       "text/plain": [
2936 |        "[OutputCasTables]\n",
2937 |        "\n",
2938 |        "             casLib     Name  Rows  Columns  \\\n",
2939 |        " 0  CASUSER(username)  summout     5       15   \n",
2940 |        " \n",
2941 |        "                                         casTable  \n",
2942 |        " 0  CASTable('summout', caslib='CASUSER(username)')  \n",
2943 |        "\n",
2944 |        "+ Elapsed: 0.0177s, user: 0.016s, sys: 0.006s, mem: 2.62mb"
2945 |       ]
2946 |      },
2947 |      "execution_count": 84,
2948 |      "metadata": {},
2949 |      "output_type": "execute_result"
2950 |     }
2951 |    ],
2952 |    "source": [
2953 |     "iris.summary(casout=outtbl)"
2954 |    ]
2955 |   },
2956 |   {
2957 |    "cell_type": "code",
2958 |    "execution_count": 85,
2959 |    "metadata": {
2960 |     "collapsed": false
2961 |    },
2962 |    "outputs": [
2963 |     {
2964 |      "data": {
2965 |       "text/html": [
2966 |        "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
2967 |        "<div class=\"cas-results-body\">\n",
2968 |        "<div>\n",
2969 |        "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table SUMMOUT</caption>\n",
2970 |        "  <thead>\n",
2971 |        "    <tr style=\"text-align: right;\">\n",
2972 |        "      <th title=\"\"></th>\n",
2973 |        "      <th title=\"_Column_\">_Column_</th>\n",
2974 |        "      <th title=\"_Min_\">_Min_</th>\n",
2975 |        "      <th title=\"_Max_\">_Max_</th>\n",
2976 |        "      <th title=\"_NObs_\">_NObs_</th>\n",
2977 |        "      <th title=\"_NMiss_\">_NMiss_</th>\n",
2978 |        "      <th title=\"_Mean_\">_Mean_</th>\n",
2979 |        "      <th title=\"_Sum_\">_Sum_</th>\n",
2980 |        "      <th title=\"_Std_\">_Std_</th>\n",
2981 |        "      <th title=\"_StdErr_\">_StdErr_</th>\n",
2982 |        "      <th title=\"_Var_\">_Var_</th>\n",
2983 |        "      <th title=\"_USS_\">_USS_</th>\n",
2984 |        "      <th title=\"_CSS_\">_CSS_</th>\n",
2985 |        "      <th title=\"_CV_\">_CV_</th>\n",
2986 |        "      <th title=\"_T_\">_T_</th>\n",
2987 |        "      <th title=\"_PRT_\">_PRT_</th>\n",
2988 |        "    </tr>\n",
2989 |        "  </thead>\n",
2990 |        "  <tbody>\n",
2991 |        "    <tr>\n",
2992 |        "      <th>0</th>\n",
2993 |        "      <td>sepal_length</td>\n",
2994 |        "      <td>6.90</td>\n",
2995 |        "      <td>7.90</td>\n",
2996 |        "      <td>15.0</td>\n",
2997 |        "      <td>0.0</td>\n",
2998 |        "      <td>7.360000</td>\n",
2999 |        "      <td>110.40</td>\n",
3000 |        "      <td>0.337639</td>\n",
3001 |        "      <td>0.087178</td>\n",
3002 |        "      <td>0.114000</td>\n",
3003 |        "      <td>814.1400</td>\n",
3004 |        "      <td>1.596000</td>\n",
3005 |        "      <td>4.587485</td>\n",
3006 |        "      <td>84.424990</td>\n",
3007 |        "      <td>2.332581e-20</td>\n",
3008 |        "    </tr>\n",
3009 |        "    <tr>\n",
3010 |        "      <th>1</th>\n",
3011 |        "      <td>sepal_width</td>\n",
3012 |        "      <td>2.60</td>\n",
3013 |        "      <td>3.80</td>\n",
3014 |        "      <td>15.0</td>\n",
3015 |        "      <td>0.0</td>\n",
3016 |        "      <td>3.126667</td>\n",
3017 |        "      <td>46.90</td>\n",
3018 |        "      <td>0.353486</td>\n",
3019 |        "      <td>0.091270</td>\n",
3020 |        "      <td>0.124952</td>\n",
3021 |        "      <td>148.3900</td>\n",
3022 |        "      <td>1.749333</td>\n",
3023 |        "      <td>11.305524</td>\n",
3024 |        "      <td>34.257443</td>\n",
3025 |        "      <td>6.662768e-15</td>\n",
3026 |        "    </tr>\n",
3027 |        "    <tr>\n",
3028 |        "      <th>2</th>\n",
3029 |        "      <td>petal_length</td>\n",
3030 |        "      <td>5.10</td>\n",
3031 |        "      <td>6.90</td>\n",
3032 |        "      <td>15.0</td>\n",
3033 |        "      <td>0.0</td>\n",
3034 |        "      <td>6.120000</td>\n",
3035 |        "      <td>91.80</td>\n",
3036 |        "      <td>0.501711</td>\n",
3037 |        "      <td>0.129541</td>\n",
3038 |        "      <td>0.251714</td>\n",
3039 |        "      <td>565.3400</td>\n",
3040 |        "      <td>3.524000</td>\n",
3041 |        "      <td>8.197898</td>\n",
3042 |        "      <td>47.243615</td>\n",
3043 |        "      <td>7.680656e-17</td>\n",
3044 |        "    </tr>\n",
3045 |        "    <tr>\n",
3046 |        "      <th>3</th>\n",
3047 |        "      <td>petal_width</td>\n",
3048 |        "      <td>1.60</td>\n",
3049 |        "      <td>2.50</td>\n",
3050 |        "      <td>15.0</td>\n",
3051 |        "      <td>0.0</td>\n",
3052 |        "      <td>2.086667</td>\n",
3053 |        "      <td>31.30</td>\n",
3054 |        "      <td>0.241622</td>\n",
3055 |        "      <td>0.062386</td>\n",
3056 |        "      <td>0.058381</td>\n",
3057 |        "      <td>66.1300</td>\n",
3058 |        "      <td>0.817333</td>\n",
3059 |        "      <td>11.579305</td>\n",
3060 |        "      <td>33.447458</td>\n",
3061 |        "      <td>9.278838e-15</td>\n",
3062 |        "    </tr>\n",
3063 |        "    <tr>\n",
3064 |        "      <th>4</th>\n",
3065 |        "      <td>length_factor</td>\n",
3066 |        "      <td>35.19</td>\n",
3067 |        "      <td>53.13</td>\n",
3068 |        "      <td>15.0</td>\n",
3069 |        "      <td>0.0</td>\n",
3070 |        "      <td>45.178667</td>\n",
3071 |        "      <td>677.68</td>\n",
3072 |        "      <td>5.527611</td>\n",
3073 |        "      <td>1.427223</td>\n",
3074 |        "      <td>30.554484</td>\n",
3075 |        "      <td>31044.4416</td>\n",
3076 |        "      <td>427.762773</td>\n",
3077 |        "      <td>12.235003</td>\n",
3078 |        "      <td>31.654945</td>\n",
3079 |        "      <td>1.987447e-14</td>\n",
3080 |        "    </tr>\n",
3081 |        "  </tbody>\n",
3082 |        "</table>\n",
3083 |        "</div>\n",
3084 |        "</div>\n",
3085 |        "<div class=\"cas-output-area\"></div>\n",
3086 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00372s</span> &#183; <span class=\"cas-user\">user 0.003s</span> &#183; <span class=\"cas-sys\">sys 0.001s</span> &#183; <span class=\"cas-memory\">mem 1.6MB</span></small></p>"
3087 |       ],
3088 |       "text/plain": [
3089 |        "[Fetch]\n",
3090 |        "\n",
3091 |        " Selected Rows from Table SUMMOUT\n",
3092 |        " \n",
3093 |        "         _Column_  _Min_  _Max_  _NObs_  _NMiss_     _Mean_   _Sum_     _Std_  \\\n",
3094 |        " 0   sepal_length   6.90   7.90    15.0      0.0   7.360000  110.40  0.337639   \n",
3095 |        " 1    sepal_width   2.60   3.80    15.0      0.0   3.126667   46.90  0.353486   \n",
3096 |        " 2   petal_length   5.10   6.90    15.0      0.0   6.120000   91.80  0.501711   \n",
3097 |        " 3    petal_width   1.60   2.50    15.0      0.0   2.086667   31.30  0.241622   \n",
3098 |        " 4  length_factor  35.19  53.13    15.0      0.0  45.178667  677.68  5.527611   \n",
3099 |        " \n",
3100 |        "    _StdErr_      _Var_       _USS_       _CSS_       _CV_        _T_  \\\n",
3101 |        " 0  0.087178   0.114000    814.1400    1.596000   4.587485  84.424990   \n",
3102 |        " 1  0.091270   0.124952    148.3900    1.749333  11.305524  34.257443   \n",
3103 |        " 2  0.129541   0.251714    565.3400    3.524000   8.197898  47.243615   \n",
3104 |        " 3  0.062386   0.058381     66.1300    0.817333  11.579305  33.447458   \n",
3105 |        " 4  1.427223  30.554484  31044.4416  427.762773  12.235003  31.654945   \n",
3106 |        " \n",
3107 |        "           _PRT_  \n",
3108 |        " 0  2.332581e-20  \n",
3109 |        " 1  6.662768e-15  \n",
3110 |        " 2  7.680656e-17  \n",
3111 |        " 3  9.278838e-15  \n",
3112 |        " 4  1.987447e-14  \n",
3113 |        "\n",
3114 |        "+ Elapsed: 0.00372s, user: 0.003s, sys: 0.001s, mem: 1.6mb"
3115 |       ]
3116 |      },
3117 |      "execution_count": 85,
3118 |      "metadata": {},
3119 |      "output_type": "execute_result"
3120 |     }
3121 |    ],
3122 |    "source": [
3123 |     "outtbl.fetch()"
3124 |    ]
3125 |   },
3126 |   {
3127 |    "cell_type": "markdown",
3128 |    "metadata": {},
3129 |    "source": [
3130 |     "### Managing Parameters using the Method Interface"
3131 |    ]
3132 |   },
3133 |   {
3134 |    "cell_type": "code",
3135 |    "execution_count": 89,
3136 |    "metadata": {
3137 |     "collapsed": false
3138 |    },
3139 |    "outputs": [
3140 |     {
3141 |      "data": {
3142 |       "text/plain": [
3143 |        "CASTable('data.iris', caslib='casuser', computedvars=['length_factor'], computedvarsprogram='length_factor = sepal_length * petal_length;', where='sepal_length > 6.8 and species = \"virginica\"')"
3144 |       ]
3145 |      },
3146 |      "execution_count": 89,
3147 |      "metadata": {},
3148 |      "output_type": "execute_result"
3149 |     }
3150 |    ],
3151 |    "source": [
3152 |     "iris = conn.CASTable('data.iris', caslib='casuser')\n",
3153 |     "iris.set_param('where', 'sepal_length > 6.8 and species = \"virginica\"')\n",
3154 |     "iris.set_param('computedvars', ['length_factor'])\n",
3155 |     "iris.set_param('computedvarsprogram', 'length_factor = sepal_length * petal_length;')\n",
3156 |     "iris"
3157 |    ]
3158 |   },
3159 |   {
3160 |    "cell_type": "code",
3161 |    "execution_count": 90,
3162 |    "metadata": {
3163 |     "collapsed": false
3164 |    },
3165 |    "outputs": [
3166 |     {
3167 |      "data": {
3168 |       "text/html": [
3169 |        "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
3170 |        "<div class=\"cas-results-body\">\n",
3171 |        "<div>\n",
3172 |        "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table DATA.IRIS</caption>\n",
3173 |        "  <thead>\n",
3174 |        "    <tr style=\"text-align: right;\">\n",
3175 |        "      <th title=\"\"></th>\n",
3176 |        "      <th title=\"sepal_length\">sepal_length</th>\n",
3177 |        "      <th title=\"petal_length\">petal_length</th>\n",
3178 |        "      <th title=\"length_factor\">length_factor</th>\n",
3179 |        "    </tr>\n",
3180 |        "  </thead>\n",
3181 |        "  <tbody>\n",
3182 |        "    <tr>\n",
3183 |        "      <th>0</th>\n",
3184 |        "      <td>6.9</td>\n",
3185 |        "      <td>5.4</td>\n",
3186 |        "      <td>37.26</td>\n",
3187 |        "    </tr>\n",
3188 |        "    <tr>\n",
3189 |        "      <th>1</th>\n",
3190 |        "      <td>6.9</td>\n",
3191 |        "      <td>5.1</td>\n",
3192 |        "      <td>35.19</td>\n",
3193 |        "    </tr>\n",
3194 |        "    <tr>\n",
3195 |        "      <th>2</th>\n",
3196 |        "      <td>7.1</td>\n",
3197 |        "      <td>5.9</td>\n",
3198 |        "      <td>41.89</td>\n",
3199 |        "    </tr>\n",
3200 |        "    <tr>\n",
3201 |        "      <th>3</th>\n",
3202 |        "      <td>7.6</td>\n",
3203 |        "      <td>6.6</td>\n",
3204 |        "      <td>50.16</td>\n",
3205 |        "    </tr>\n",
3206 |        "    <tr>\n",
3207 |        "      <th>4</th>\n",
3208 |        "      <td>7.3</td>\n",
3209 |        "      <td>6.3</td>\n",
3210 |        "      <td>45.99</td>\n",
3211 |        "    </tr>\n",
3212 |        "  </tbody>\n",
3213 |        "</table>\n",
3214 |        "</div>\n",
3215 |        "</div>\n",
3216 |        "<div class=\"cas-output-area\"></div>\n",
3217 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0137s</span> &#183; <span class=\"cas-user\">user 0.011s</span> &#183; <span class=\"cas-sys\">sys 0.005s</span> &#183; <span class=\"cas-memory\">mem 2.47MB</span></small></p>"
3218 |       ],
3219 |       "text/plain": [
3220 |        "[Fetch]\n",
3221 |        "\n",
3222 |        " Selected Rows from Table DATA.IRIS\n",
3223 |        " \n",
3224 |        "    sepal_length  petal_length  length_factor\n",
3225 |        " 0           6.9           5.4          37.26\n",
3226 |        " 1           6.9           5.1          35.19\n",
3227 |        " 2           7.1           5.9          41.89\n",
3228 |        " 3           7.6           6.6          50.16\n",
3229 |        " 4           7.3           6.3          45.99\n",
3230 |        "\n",
3231 |        "+ Elapsed: 0.0137s, user: 0.011s, sys: 0.005s, mem: 2.47mb"
3232 |       ]
3233 |      },
3234 |      "execution_count": 90,
3235 |      "metadata": {},
3236 |      "output_type": "execute_result"
3237 |     }
3238 |    ],
3239 |    "source": [
3240 |     "iris.fetch(to=5, fetchvars=['sepal_length', 'petal_length', 'length_factor'])"
3241 |    ]
3242 |   },
3243 |   {
3244 |    "cell_type": "markdown",
3245 |    "metadata": {},
3246 |    "source": [
3247 |     "All of the following are equivalent ways of setting parameters."
3248 |    ]
3249 |   },
3250 |   {
3251 |    "cell_type": "code",
3252 |    "execution_count": 91,
3253 |    "metadata": {
3254 |     "collapsed": false
3255 |    },
3256 |    "outputs": [
3257 |     {
3258 |      "data": {
3259 |       "text/plain": [
3260 |        "CASTable('data.iris', caslib='casuser', computedvars=['length_factor'], computedvarsprogram='length_factor = sepal_length * petal_length;', where='sepal_length > 6.8 and species = \"virginica\"')"
3261 |       ]
3262 |      },
3263 |      "execution_count": 91,
3264 |      "metadata": {},
3265 |      "output_type": "execute_result"
3266 |     }
3267 |    ],
3268 |    "source": [
3269 |     "iris.set_params('where', 'sepal_length > 6.8 and species = \"virginica\"',\n",
3270 |     "                'computedvars', ['length_factor'],\n",
3271 |     "                'computedvarsprogram', 'length_factor = sepal_length * petal_length;')\n",
3272 |     "iris"
3273 |    ]
3274 |   },
3275 |   {
3276 |    "cell_type": "code",
3277 |    "execution_count": 92,
3278 |    "metadata": {
3279 |     "collapsed": false
3280 |    },
3281 |    "outputs": [
3282 |     {
3283 |      "data": {
3284 |       "text/plain": [
3285 |        "CASTable('data.iris', caslib='casuser', computedvars=['length_factor'], computedvarsprogram='length_factor = sepal_length * petal_length;', where='sepal_length > 6.8 and species = \"virginica\"')"
3286 |       ]
3287 |      },
3288 |      "execution_count": 92,
3289 |      "metadata": {},
3290 |      "output_type": "execute_result"
3291 |     }
3292 |    ],
3293 |    "source": [
3294 |     "iris.set_params(('where', 'sepal_length > 6.8 and species = \"virginica\"'),\n",
3295 |     "                ('computedvars', ['length_factor']),\n",
3296 |     "                ('computedvarsprogram', 'length_factor = sepal_length * petal_length;'))\n",
3297 |     "iris"
3298 |    ]
3299 |   },
3300 |   {
3301 |    "cell_type": "code",
3302 |    "execution_count": 93,
3303 |    "metadata": {
3304 |     "collapsed": false
3305 |    },
3306 |    "outputs": [
3307 |     {
3308 |      "data": {
3309 |       "text/plain": [
3310 |        "CASTable('data.iris', caslib='casuser', computedvars=['length_factor'], computedvarsprogram='length_factor = sepal_length * petal_length;', where='sepal_length > 6.8 and species = \"virginica\"')"
3311 |       ]
3312 |      },
3313 |      "execution_count": 93,
3314 |      "metadata": {},
3315 |      "output_type": "execute_result"
3316 |     }
3317 |    ],
3318 |    "source": [
3319 |     "iris.set_params(where='sepal_length > 6.8 and species = \"virginica\"',\n",
3320 |     "                computedvars=['length_factor'],\n",
3321 |     "                computedvarsprogram='length_factor = sepal_length * petal_length;')\n",
3322 |     "iris"
3323 |    ]
3324 |   },
3325 |   {
3326 |    "cell_type": "code",
3327 |    "execution_count": 94,
3328 |    "metadata": {
3329 |     "collapsed": false
3330 |    },
3331 |    "outputs": [
3332 |     {
3333 |      "data": {
3334 |       "text/plain": [
3335 |        "CASTable('data.iris', caslib='casuser', computedvars=['length_factor'], computedvarsprogram='length_factor = sepal_length * petal_length;', where='sepal_length > 6.8 and species = \"virginica\"')"
3336 |       ]
3337 |      },
3338 |      "execution_count": 94,
3339 |      "metadata": {},
3340 |      "output_type": "execute_result"
3341 |     }
3342 |    ],
3343 |    "source": [
3344 |     "iris.set_params({'where': 'sepal_length > 6.8 and species = \"virginica\"',\n",
3345 |     "                 'computedvars': ['length_factor'],\n",
3346 |     "                 'computedvarsprogram': 'length_factor = sepal_length * petal_length;'})\n",
3347 |     "iris"
3348 |    ]
3349 |   },
3350 |   {
3351 |    "cell_type": "markdown",
3352 |    "metadata": {},
3353 |    "source": [
3354 |     "Checking and retrieving parameters."
3355 |    ]
3356 |   },
3357 |   {
3358 |    "cell_type": "code",
3359 |    "execution_count": 95,
3360 |    "metadata": {
3361 |     "collapsed": false
3362 |    },
3363 |    "outputs": [
3364 |     {
3365 |      "data": {
3366 |       "text/plain": [
3367 |        "True"
3368 |       ]
3369 |      },
3370 |      "execution_count": 95,
3371 |      "metadata": {},
3372 |      "output_type": "execute_result"
3373 |     }
3374 |    ],
3375 |    "source": [
3376 |     "iris.has_param('where')"
3377 |    ]
3378 |   },
3379 |   {
3380 |    "cell_type": "code",
3381 |    "execution_count": 96,
3382 |    "metadata": {
3383 |     "collapsed": false
3384 |    },
3385 |    "outputs": [
3386 |     {
3387 |      "data": {
3388 |       "text/plain": [
3389 |        "False"
3390 |       ]
3391 |      },
3392 |      "execution_count": 96,
3393 |      "metadata": {},
3394 |      "output_type": "execute_result"
3395 |     }
3396 |    ],
3397 |    "source": [
3398 |     "iris.has_param('groupby')"
3399 |    ]
3400 |   },
3401 |   {
3402 |    "cell_type": "code",
3403 |    "execution_count": 97,
3404 |    "metadata": {
3405 |     "collapsed": false
3406 |    },
3407 |    "outputs": [
3408 |     {
3409 |      "data": {
3410 |       "text/plain": [
3411 |        "'sepal_length > 6.8 and species = \"virginica\"'"
3412 |       ]
3413 |      },
3414 |      "execution_count": 97,
3415 |      "metadata": {},
3416 |      "output_type": "execute_result"
3417 |     }
3418 |    ],
3419 |    "source": [
3420 |     "iris.get_param('where')"
3421 |    ]
3422 |   },
3423 |   {
3424 |    "cell_type": "code",
3425 |    "execution_count": 98,
3426 |    "metadata": {
3427 |     "collapsed": false
3428 |    },
3429 |    "outputs": [
3430 |     {
3431 |      "data": {
3432 |       "text/plain": [
3433 |        "{'computedvars': ['length_factor'],\n",
3434 |        " 'where': 'sepal_length > 6.8 and species = \"virginica\"'}"
3435 |       ]
3436 |      },
3437 |      "execution_count": 98,
3438 |      "metadata": {},
3439 |      "output_type": "execute_result"
3440 |     }
3441 |    ],
3442 |    "source": [
3443 |     "iris.get_params('where', 'computedvars')"
3444 |    ]
3445 |   },
3446 |   {
3447 |    "cell_type": "markdown",
3448 |    "metadata": {},
3449 |    "source": [
3450 |     "Deleting parameters."
3451 |    ]
3452 |   },
3453 |   {
3454 |    "cell_type": "code",
3455 |    "execution_count": 99,
3456 |    "metadata": {
3457 |     "collapsed": false
3458 |    },
3459 |    "outputs": [
3460 |     {
3461 |      "data": {
3462 |       "text/plain": [
3463 |        "CASTable('data.iris', caslib='casuser', computedvars=['length_factor'], computedvarsprogram='length_factor = sepal_length * petal_length;', where='sepal_length > 6.8 and species = \"virginica\"')"
3464 |       ]
3465 |      },
3466 |      "execution_count": 99,
3467 |      "metadata": {},
3468 |      "output_type": "execute_result"
3469 |     }
3470 |    ],
3471 |    "source": [
3472 |     "iris"
3473 |    ]
3474 |   },
3475 |   {
3476 |    "cell_type": "code",
3477 |    "execution_count": 100,
3478 |    "metadata": {
3479 |     "collapsed": false
3480 |    },
3481 |    "outputs": [
3482 |     {
3483 |      "data": {
3484 |       "text/plain": [
3485 |        "CASTable('data.iris', caslib='casuser', where='sepal_length > 6.8 and species = \"virginica\"')"
3486 |       ]
3487 |      },
3488 |      "execution_count": 100,
3489 |      "metadata": {},
3490 |      "output_type": "execute_result"
3491 |     }
3492 |    ],
3493 |    "source": [
3494 |     "iris.del_params('computedvars', 'computedvarsprogram')\n",
3495 |     "iris"
3496 |    ]
3497 |   },
3498 |   {
3499 |    "cell_type": "markdown",
3500 |    "metadata": {},
3501 |    "source": [
3502 |     "### Managing Parameters using the Attribute Interface"
3503 |    ]
3504 |   },
3505 |   {
3506 |    "cell_type": "code",
3507 |    "execution_count": 101,
3508 |    "metadata": {
3509 |     "collapsed": false
3510 |    },
3511 |    "outputs": [
3512 |     {
3513 |      "data": {
3514 |       "text/plain": [
3515 |        "CASTable('data.iris', caslib='casuser')"
3516 |       ]
3517 |      },
3518 |      "execution_count": 101,
3519 |      "metadata": {},
3520 |      "output_type": "execute_result"
3521 |     }
3522 |    ],
3523 |    "source": [
3524 |     "iris = conn.CASTable('data.iris', caslib='casuser')\n",
3525 |     "iris"
3526 |    ]
3527 |   },
3528 |   {
3529 |    "cell_type": "code",
3530 |    "execution_count": 102,
3531 |    "metadata": {
3532 |     "collapsed": false
3533 |    },
3534 |    "outputs": [
3535 |     {
3536 |      "data": {
3537 |       "text/plain": [
3538 |        "CASTable('data.iris', caslib='casuser', where='sepal_length > 6.8 and species = \"virginica\"')"
3539 |       ]
3540 |      },
3541 |      "execution_count": 102,
3542 |      "metadata": {},
3543 |      "output_type": "execute_result"
3544 |     }
3545 |    ],
3546 |    "source": [
3547 |     "iris.params.where = 'sepal_length > 6.8 and species = \"virginica\"'\n",
3548 |     "iris"
3549 |    ]
3550 |   },
3551 |   {
3552 |    "cell_type": "code",
3553 |    "execution_count": 103,
3554 |    "metadata": {
3555 |     "collapsed": false
3556 |    },
3557 |    "outputs": [
3558 |     {
3559 |      "data": {
3560 |       "text/plain": [
3561 |        "CASTable('data.iris', caslib='casuser', groupby=['species', 'sepal_length'], where='sepal_length > 6.8 and species = \"virginica\"')"
3562 |       ]
3563 |      },
3564 |      "execution_count": 103,
3565 |      "metadata": {},
3566 |      "output_type": "execute_result"
3567 |     }
3568 |    ],
3569 |    "source": [
3570 |     "iris.params.groupby[0] = 'species'\n",
3571 |     "iris.params.groupby[1] = 'sepal_length'\n",
3572 |     "iris"
3573 |    ]
3574 |   },
3575 |   {
3576 |    "cell_type": "code",
3577 |    "execution_count": 104,
3578 |    "metadata": {
3579 |     "collapsed": false
3580 |    },
3581 |    "outputs": [
3582 |     {
3583 |      "data": {
3584 |       "text/plain": [
3585 |        "CASTable('data.iris', caslib='casuser')"
3586 |       ]
3587 |      },
3588 |      "execution_count": 104,
3589 |      "metadata": {},
3590 |      "output_type": "execute_result"
3591 |     }
3592 |    ],
3593 |    "source": [
3594 |     "del iris.params.groupby\n",
3595 |     "del iris.params.where\n",
3596 |     "iris"
3597 |    ]
3598 |   },
3599 |   {
3600 |    "cell_type": "markdown",
3601 |    "metadata": {},
3602 |    "source": [
3603 |     "Parameters can be set directly on the **CASTable** object as well."
3604 |    ]
3605 |   },
3606 |   {
3607 |    "cell_type": "code",
3608 |    "execution_count": 105,
3609 |    "metadata": {
3610 |     "collapsed": false
3611 |    },
3612 |    "outputs": [
3613 |     {
3614 |      "data": {
3615 |       "text/plain": [
3616 |        "CASTable('data.iris', caslib='casuser', groupby=['species'], where='sepal_length > 6.8 and species = \"virginica\"')"
3617 |       ]
3618 |      },
3619 |      "execution_count": 105,
3620 |      "metadata": {},
3621 |      "output_type": "execute_result"
3622 |     }
3623 |    ],
3624 |    "source": [
3625 |     "iris.groupby = ['species']\n",
3626 |     "iris.where = 'sepal_length > 6.8 and species = \"virginica\"'\n",
3627 |     "iris"
3628 |    ]
3629 |   },
3630 |   {
3631 |    "cell_type": "markdown",
3632 |    "metadata": {},
3633 |    "source": [
3634 |     "### Persisting **CASTable** Parameters"
3635 |    ]
3636 |   },
3637 |   {
3638 |    "cell_type": "code",
3639 |    "execution_count": 107,
3640 |    "metadata": {
3641 |     "collapsed": false
3642 |    },
3643 |    "outputs": [
3644 |     {
3645 |      "data": {
3646 |       "text/plain": [
3647 |        "CASTable('data.iris', caslib='casuser', groupby=['species'], where='sepal_length > 6.8 and species = \"virginica\"')"
3648 |       ]
3649 |      },
3650 |      "execution_count": 107,
3651 |      "metadata": {},
3652 |      "output_type": "execute_result"
3653 |     }
3654 |    ],
3655 |    "source": [
3656 |     "iris"
3657 |    ]
3658 |   },
3659 |   {
3660 |    "cell_type": "code",
3661 |    "execution_count": 108,
3662 |    "metadata": {
3663 |     "collapsed": false
3664 |    },
3665 |    "outputs": [
3666 |     {
3667 |      "data": {
3668 |       "text/html": [
3669 |        "<div class=\"cas-results-key\"><b>&#167; caslib</b></div>\n",
3670 |        "<div class=\"cas-results-body\">\n",
3671 |        "<div>CASUSER(username)</div>\n",
3672 |        "</div>\n",
3673 |        "<div class=\"cas-results-key\"><hr/><b>&#167; tableName</b></div>\n",
3674 |        "<div class=\"cas-results-body\">\n",
3675 |        "<div>_T_RBC6QJBX_6HFJ3SUB_ZDE6BCRWX4</div>\n",
3676 |        "</div>\n",
3677 |        "<div class=\"cas-results-key\"><hr/><b>&#167; rowsTransferred</b></div>\n",
3678 |        "<div class=\"cas-results-body\">\n",
3679 |        "<div>0</div>\n",
3680 |        "</div>\n",
3681 |        "<div class=\"cas-results-key\"><hr/><b>&#167; shuffleWaitTime</b></div>\n",
3682 |        "<div class=\"cas-results-body\">\n",
3683 |        "<div>0.0</div>\n",
3684 |        "</div>\n",
3685 |        "<div class=\"cas-results-key\"><hr/><b>&#167; minShuffleWaitTime</b></div>\n",
3686 |        "<div class=\"cas-results-body\">\n",
3687 |        "<div>1e+300</div>\n",
3688 |        "</div>\n",
3689 |        "<div class=\"cas-results-key\"><hr/><b>&#167; maxShuffleWaitTime</b></div>\n",
3690 |        "<div class=\"cas-results-body\">\n",
3691 |        "<div>0.0</div>\n",
3692 |        "</div>\n",
3693 |        "<div class=\"cas-results-key\"><hr/><b>&#167; averageShuffleWaitTime</b></div>\n",
3694 |        "<div class=\"cas-results-body\">\n",
3695 |        "<div>0.0</div>\n",
3696 |        "</div>\n",
3697 |        "<div class=\"cas-results-key\"><hr/><b>&#167; casTable</b></div>\n",
3698 |        "<div class=\"cas-results-body\">\n",
3699 |        "<div>CASTable('_T_RBC6QJBX_6HFJ3SUB_ZDE6BCRWX4', caslib='CASUSER(username)')</div>\n",
3700 |        "</div>\n",
3701 |        "<div class=\"cas-output-area\"></div>\n",
3702 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0036s</span> &#183; <span class=\"cas-user\">user 0.002s</span> &#183; <span class=\"cas-sys\">sys 0.001s</span> &#183; <span class=\"cas-memory\">mem 1.63MB</span></small></p>"
3703 |       ],
3704 |       "text/plain": [
3705 |        "[caslib]\n",
3706 |        "\n",
3707 |        " 'CASUSER(username)'\n",
3708 |        "\n",
3709 |        "[tableName]\n",
3710 |        "\n",
3711 |        " '_T_RBC6QJBX_6HFJ3SUB_ZDE6BCRWX4'\n",
3712 |        "\n",
3713 |        "[rowsTransferred]\n",
3714 |        "\n",
3715 |        " 0\n",
3716 |        "\n",
3717 |        "[shuffleWaitTime]\n",
3718 |        "\n",
3719 |        " 0.0\n",
3720 |        "\n",
3721 |        "[minShuffleWaitTime]\n",
3722 |        "\n",
3723 |        " 1e+300\n",
3724 |        "\n",
3725 |        "[maxShuffleWaitTime]\n",
3726 |        "\n",
3727 |        " 0.0\n",
3728 |        "\n",
3729 |        "[averageShuffleWaitTime]\n",
3730 |        "\n",
3731 |        " 0.0\n",
3732 |        "\n",
3733 |        "[casTable]\n",
3734 |        "\n",
3735 |        " CASTable('_T_RBC6QJBX_6HFJ3SUB_ZDE6BCRWX4', caslib='CASUSER(username)')\n",
3736 |        "\n",
3737 |        "+ Elapsed: 0.0036s, user: 0.002s, sys: 0.001s, mem: 1.63mb"
3738 |       ]
3739 |      },
3740 |      "execution_count": 108,
3741 |      "metadata": {},
3742 |      "output_type": "execute_result"
3743 |     }
3744 |    ],
3745 |    "source": [
3746 |     "sub_iris = iris.partition()\n",
3747 |     "sub_iris"
3748 |    ]
3749 |   },
3750 |   {
3751 |    "cell_type": "code",
3752 |    "execution_count": 109,
3753 |    "metadata": {
3754 |     "collapsed": true
3755 |    },
3756 |    "outputs": [],
3757 |    "source": [
3758 |     "sub_iris = sub_iris.casTable"
3759 |    ]
3760 |   },
3761 |   {
3762 |    "cell_type": "code",
3763 |    "execution_count": 110,
3764 |    "metadata": {
3765 |     "collapsed": false
3766 |    },
3767 |    "outputs": [
3768 |     {
3769 |      "data": {
3770 |       "text/html": [
3771 |        "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
3772 |        "<div class=\"cas-results-body\">\n",
3773 |        "<div>\n",
3774 |        "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table _T_RBC6QJBX_6HFJ3SUB_ZDE6BCRWX4</caption>\n",
3775 |        "  <thead>\n",
3776 |        "    <tr style=\"text-align: right;\">\n",
3777 |        "      <th title=\"\"></th>\n",
3778 |        "      <th title=\"sepal_length\">sepal_length</th>\n",
3779 |        "      <th title=\"sepal_width\">sepal_width</th>\n",
3780 |        "      <th title=\"petal_length\">petal_length</th>\n",
3781 |        "      <th title=\"petal_width\">petal_width</th>\n",
3782 |        "      <th title=\"species\">species</th>\n",
3783 |        "    </tr>\n",
3784 |        "  </thead>\n",
3785 |        "  <tbody>\n",
3786 |        "    <tr>\n",
3787 |        "      <th>0</th>\n",
3788 |        "      <td>6.9</td>\n",
3789 |        "      <td>3.1</td>\n",
3790 |        "      <td>5.4</td>\n",
3791 |        "      <td>2.1</td>\n",
3792 |        "      <td>virginica</td>\n",
3793 |        "    </tr>\n",
3794 |        "    <tr>\n",
3795 |        "      <th>1</th>\n",
3796 |        "      <td>6.9</td>\n",
3797 |        "      <td>3.1</td>\n",
3798 |        "      <td>5.1</td>\n",
3799 |        "      <td>2.3</td>\n",
3800 |        "      <td>virginica</td>\n",
3801 |        "    </tr>\n",
3802 |        "    <tr>\n",
3803 |        "      <th>2</th>\n",
3804 |        "      <td>7.1</td>\n",
3805 |        "      <td>3.0</td>\n",
3806 |        "      <td>5.9</td>\n",
3807 |        "      <td>2.1</td>\n",
3808 |        "      <td>virginica</td>\n",
3809 |        "    </tr>\n",
3810 |        "    <tr>\n",
3811 |        "      <th>3</th>\n",
3812 |        "      <td>7.6</td>\n",
3813 |        "      <td>3.0</td>\n",
3814 |        "      <td>6.6</td>\n",
3815 |        "      <td>2.1</td>\n",
3816 |        "      <td>virginica</td>\n",
3817 |        "    </tr>\n",
3818 |        "    <tr>\n",
3819 |        "      <th>4</th>\n",
3820 |        "      <td>7.3</td>\n",
3821 |        "      <td>2.9</td>\n",
3822 |        "      <td>6.3</td>\n",
3823 |        "      <td>1.8</td>\n",
3824 |        "      <td>virginica</td>\n",
3825 |        "    </tr>\n",
3826 |        "    <tr>\n",
3827 |        "      <th>5</th>\n",
3828 |        "      <td>7.2</td>\n",
3829 |        "      <td>3.6</td>\n",
3830 |        "      <td>6.1</td>\n",
3831 |        "      <td>2.5</td>\n",
3832 |        "      <td>virginica</td>\n",
3833 |        "    </tr>\n",
3834 |        "    <tr>\n",
3835 |        "      <th>6</th>\n",
3836 |        "      <td>7.7</td>\n",
3837 |        "      <td>3.8</td>\n",
3838 |        "      <td>6.7</td>\n",
3839 |        "      <td>2.2</td>\n",
3840 |        "      <td>virginica</td>\n",
3841 |        "    </tr>\n",
3842 |        "    <tr>\n",
3843 |        "      <th>7</th>\n",
3844 |        "      <td>7.7</td>\n",
3845 |        "      <td>2.6</td>\n",
3846 |        "      <td>6.9</td>\n",
3847 |        "      <td>2.3</td>\n",
3848 |        "      <td>virginica</td>\n",
3849 |        "    </tr>\n",
3850 |        "    <tr>\n",
3851 |        "      <th>8</th>\n",
3852 |        "      <td>6.9</td>\n",
3853 |        "      <td>3.2</td>\n",
3854 |        "      <td>5.7</td>\n",
3855 |        "      <td>2.3</td>\n",
3856 |        "      <td>virginica</td>\n",
3857 |        "    </tr>\n",
3858 |        "    <tr>\n",
3859 |        "      <th>9</th>\n",
3860 |        "      <td>7.7</td>\n",
3861 |        "      <td>2.8</td>\n",
3862 |        "      <td>6.7</td>\n",
3863 |        "      <td>2.0</td>\n",
3864 |        "      <td>virginica</td>\n",
3865 |        "    </tr>\n",
3866 |        "    <tr>\n",
3867 |        "      <th>10</th>\n",
3868 |        "      <td>7.2</td>\n",
3869 |        "      <td>3.2</td>\n",
3870 |        "      <td>6.0</td>\n",
3871 |        "      <td>1.8</td>\n",
3872 |        "      <td>virginica</td>\n",
3873 |        "    </tr>\n",
3874 |        "    <tr>\n",
3875 |        "      <th>11</th>\n",
3876 |        "      <td>7.2</td>\n",
3877 |        "      <td>3.0</td>\n",
3878 |        "      <td>5.8</td>\n",
3879 |        "      <td>1.6</td>\n",
3880 |        "      <td>virginica</td>\n",
3881 |        "    </tr>\n",
3882 |        "    <tr>\n",
3883 |        "      <th>12</th>\n",
3884 |        "      <td>7.4</td>\n",
3885 |        "      <td>2.8</td>\n",
3886 |        "      <td>6.1</td>\n",
3887 |        "      <td>1.9</td>\n",
3888 |        "      <td>virginica</td>\n",
3889 |        "    </tr>\n",
3890 |        "    <tr>\n",
3891 |        "      <th>13</th>\n",
3892 |        "      <td>7.9</td>\n",
3893 |        "      <td>3.8</td>\n",
3894 |        "      <td>6.4</td>\n",
3895 |        "      <td>2.0</td>\n",
3896 |        "      <td>virginica</td>\n",
3897 |        "    </tr>\n",
3898 |        "    <tr>\n",
3899 |        "      <th>14</th>\n",
3900 |        "      <td>7.7</td>\n",
3901 |        "      <td>3.0</td>\n",
3902 |        "      <td>6.1</td>\n",
3903 |        "      <td>2.3</td>\n",
3904 |        "      <td>virginica</td>\n",
3905 |        "    </tr>\n",
3906 |        "  </tbody>\n",
3907 |        "</table>\n",
3908 |        "</div>\n",
3909 |        "</div>\n",
3910 |        "<div class=\"cas-output-area\"></div>\n",
3911 |        "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00331s</span> &#183; <span class=\"cas-user\">user 0.002s</span> &#183; <span class=\"cas-sys\">sys 0.001s</span> &#183; <span class=\"cas-memory\">mem 1.58MB</span></small></p>"
3912 |       ],
3913 |       "text/plain": [
3914 |        "[Fetch]\n",
3915 |        "\n",
3916 |        " Selected Rows from Table _T_RBC6QJBX_6HFJ3SUB_ZDE6BCRWX4\n",
3917 |        " \n",
3918 |        "     sepal_length  sepal_width  petal_length  petal_width    species\n",
3919 |        " 0            6.9          3.1           5.4          2.1  virginica\n",
3920 |        " 1            6.9          3.1           5.1          2.3  virginica\n",
3921 |        " 2            7.1          3.0           5.9          2.1  virginica\n",
3922 |        " 3            7.6          3.0           6.6          2.1  virginica\n",
3923 |        " 4            7.3          2.9           6.3          1.8  virginica\n",
3924 |        " 5            7.2          3.6           6.1          2.5  virginica\n",
3925 |        " 6            7.7          3.8           6.7          2.2  virginica\n",
3926 |        " 7            7.7          2.6           6.9          2.3  virginica\n",
3927 |        " 8            6.9          3.2           5.7          2.3  virginica\n",
3928 |        " 9            7.7          2.8           6.7          2.0  virginica\n",
3929 |        " 10           7.2          3.2           6.0          1.8  virginica\n",
3930 |        " 11           7.2          3.0           5.8          1.6  virginica\n",
3931 |        " 12           7.4          2.8           6.1          1.9  virginica\n",
3932 |        " 13           7.9          3.8           6.4          2.0  virginica\n",
3933 |        " 14           7.7          3.0           6.1          2.3  virginica\n",
3934 |        "\n",
3935 |        "+ Elapsed: 0.00331s, user: 0.002s, sys: 0.001s, mem: 1.58mb"
3936 |       ]
3937 |      },
3938 |      "execution_count": 110,
3939 |      "metadata": {},
3940 |      "output_type": "execute_result"
3941 |     }
3942 |    ],
3943 |    "source": [
3944 |     "sub_iris.fetch()"
3945 |    ]
3946 |   },
3947 |   {
3948 |    "cell_type": "code",
3949 |    "execution_count": null,
3950 |    "metadata": {
3951 |     "collapsed": true
3952 |    },
3953 |    "outputs": [],
3954 |    "source": []
3955 |   }
3956 |  ],
3957 |  "metadata": {
3958 |   "kernelspec": {
3959 |    "display_name": "Python 3",
3960 |    "language": "python",
3961 |    "name": "python3"
3962 |   },
3963 |   "language_info": {
3964 |    "codemirror_mode": {
3965 |     "name": "ipython",
3966 |     "version": 3
3967 |    },
3968 |    "file_extension": ".py",
3969 |    "mimetype": "text/x-python",
3970 |    "name": "python",
3971 |    "nbconvert_exporter": "python",
3972 |    "pygments_lexer": "ipython3",
3973 |    "version": "3.4.5"
3974 |   }
3975 |  },
3976 |  "nbformat": 4,
3977 |  "nbformat_minor": 0
3978 | }
3979 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | <img src='cover.jpg' align='right' />
 2 | 
 3 | # SAS Viya: The Python Perspective
 4 | 
 5 | Kevin D Smith & Xiangxiang Meng
 6 | 
 7 | This project contains the sample code for the book *SAS Viya: The Python
 8 | Perspective*.  All examples are in the form of
 9 | [Jupyter notebooks](http://jupyter.org).
10 | 
11 | ## Description
12 | 
13 | *Learn how to access analytics from SAS Cloud Analytic Services (CAS)
14 | using Python and the SAS® Viya™ platform.*
15 | 
16 | SAS® Viya™: The Python Perspective is an introduction to using the
17 | Python client on the SAS Viya platform. SAS Viya is a high-performance, 
18 | fault-tolerant analytics architecture that can be deployed on both 
19 | public and private cloud infrastructures. While SAS Viya can be used by 
20 | various SAS applications, it also enables you to access analytic methods 
21 | from SAS, Python, Lua, and Java, as well as through a REST interface 
22 | using HTTP or HTTPS.
23 | 
24 | This book focuses on the perspective of SAS Viya from Python. SAS Viya 
25 | is made up of multiple components. The central piece of this ecosystem 
26 | is SAS Cloud Analytic Services (CAS). CAS is the cloud-based server 
27 | that all clients communicate with to run analytical methods. The Python 
28 | client is used to drive the CAS component directly using objects and 
29 | constructs that are familiar to Python programmers.
30 | 
31 | Some knowledge of Python would be helpful before using this book;
32 | however, there is an appendix that covers the features of Python that are 
33 | used in the CAS Python client. Knowledge of CAS is not required to use 
34 | this book. However, you will need to have a CAS server set up and 
35 | running to execute the examples in this book.
36 | 
37 | With this book, you will learn how to:
38 | 
39 | * Install the required components for accessing CAS from Python
40 | * Connect to CAS, load data, and run simple analyses
41 | * Work with CAS using APIs familiar to Python users
42 | * Learn about general CAS workflows and advanced features of the CAS Python client
43 | 
44 | SAS® Viya™: The Python Perspective covers topics that will be useful to
45 | beginners as well as experienced CAS users. It includes examples from 
46 | creating connections to CAS all the way to simple statistics and machine 
47 | learning, but it is also useful as a desktop reference.
48 | 
49 | ## Details
50 | 
51 | * *Epub* ISBN# 978-1-62960-883-9
52 | * *Mobi* ISBN# 978-1-62960-884-6
53 | * *PDF* ISBN# 978-1-62960-885-3
54 | * *Hardcopy* ISBN# 978-1-62960-276-9
55 | * *Pages* 306
56 | 
57 | - [Book Excerpt](https://www.sas.com/storefront/aux/en/splcpython/69194_excerpt.pdf)
58 | - [Table of Contents](https://www.sas.com/storefront/aux/en/splcpython/69194_toc.pdf)
59 | 


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/data/README.md:
--------------------------------------------------------------------------------
1 | # Organics Data Set
2 | 
3 | This directory contains the organics data set used in later chapters in
4 | the book.  It has been compressed to reduce the download size.  To use
5 | it, simply download the file to a location on your CAS server, decompress
6 | it using your favorite ZIP program, and load it into the server using the
7 | `table.loadtable` CAS action.
8 | 


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/data/organics.csv.zip:
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