examples/Examples-Section3-SGF2020-Tutorial-Everything_Is_Better_With_Friends.ipynb

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   "source": [
    "# Everything is better with friends: Using SAS in Python applications with SASPy and open-source tools\n",
    "\n",
    "## Half-Day Tutorial &nbsp;&bullet;&nbsp;  SAS Global Forum 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 3. Executing SAS Procedures with Convenience Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 3.1. Connect Directly to a SAS Dataset\n",
    "\n",
    "<b><u>Instructions</u></b>: Click anywhere in the code cell immediately below, and run the cell using Shift-Enter. Then attempt the Exercises that follow, only looking at the explanatory notes for hints when needed."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "from saspy import SASsession\n",
    "sas = SASsession(results='TEXT')\n",
    "\n",
    "fish_sds = sas.sasdata(table='fish', libref='sashelp')\n",
    "print(type(fish_sds))\n",
    "print()\n",
    "fish_sds.columnInfo()\n",
    "print()\n",
    "fish_sds.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Line-by-Line Code Explanation**:\n",
    "\n",
    "* Lines 1-2: Import the `SASsession` object data type from the `saspy` module, and create a `SASsession` object named `sas` with results in TEXT format.\n",
    "\n",
    "* Line 4: Create a file pointer to the SAS dataset sashelp.fish (a physical file on disk).\n",
    "\n",
    "* Lines 5-9: Print the type of file pointer `fish_sds`, following by a blank line, metadata for the SAS dataset, another blank line, and summary statistics for the numeric variables in the SAS dataset.\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 3.1.1**. True or False: The `sasdata` method imports a SAS dataset and returns a Python DataFrame.\n",
    "  \n",
    "&nbsp;\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 3.1.2**. The `sasdata` method allows SAS procedures to be executed using Python methods. Can you guess which procedures are invoked by the `columnInfo` and `describe` methods?\n",
    "  \n",
    "&nbsp;\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notes About Example 3.1**:\n",
    "\n",
    "1. The `sasdata` creates a file pointer, meaning a direct connection to a disk-based SAS dataset, whereas the `sasdata2dataframe` method used in Example 2.2 loaded a SAS dataset into memory as a DataFrame.\n",
    "\n",
    "2. `columnInfo` and `describe` are examples of \"convenience method,\" which can be used to implicitly the invoke SAS's CONTENTS procedure and MEANS procedure, respectively. \n",
    "\n",
    "3. Additional convenience methods are listed in the SASPy documentation at <https://sassoftware.github.io/saspy/api.html#sas-data-object>.\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 3.2 Display Generated SAS Code\n",
    "\n",
    "<b><u>Instructions</u></b>: Click anywhere in the code cell immediately below, and run the cell using Shift-Enter. Then attempt the Exercises that follow, only looking at the explanatory notes for hints when needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sas.teach_me_SAS(True)\n",
    "fish_sds.describe()\n",
    "sas.teach_me_SAS(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Line-by-Line Code Explanation**:\n",
    "\n",
    "* Line 1: Set `teach_me_SAS` to `True`. (This is a global effect that will apply to all saspy method calls submitted from this point forward in the current SAS session.)\n",
    "\n",
    "* Line 2: Invoke the `describe` method. (Because `teach_me_SAS` is set to True, the SAS code generated by the `describe` method is displayed, but not executed.)\n",
    "\n",
    "* Line 3: Set `teach_me_SAS` to False, reversing the effect in Line 1.\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 3.2.1**. Imagine `teach_me_SAS` had been set to True when the `columnInfo` method was called in Example 3.1. What SAS code might have been displayed instead?\n",
    "\n",
    "&nbsp;\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 3.2.2**. Write several lines of Python code to execute your SAS code from Exercise 3.2.1, and to then display the results in TEXT format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notes About Example 3.2**:\n",
    "\n",
    "1. Lines 1 and 3 can be thought of as a \"Teach Me SAS\" sandwich, similar to how an \"ODS Sandwich\" can be used to toggle output to a specific destination.\n",
    "\n",
    "2. `True` and `False` are standard Python objects. Like their SAS equivalents, they are interchangeable with the values `1` and `0`, respectively.\n",
    "\n",
    "3. The `teach_me_SAS` method allows us to extract (and modify) the SAS code generated by convenience methods. For example, the `describe` convenience method doesn't allow us to set classification variables for PROC MEANS. However, we can use `teach_me_SAS` to generate the underlying SAS code, add a class statement, and then execute the modified SAS code using the `submit` method (as in Example 2.4).\n",
    "\n",
    "4. The `sas` object represents a connection to a SAS session and was created in Exercise 3.1 above.\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 3.3 Don't Worry! You have options...\n",
    "\n",
    "<b><u>Instructions</u></b>: Click anywhere in the code cell immediately below, and run the cell using Shift-Enter. Then attempt the Exercises that follow, only looking at the explanatory notes for hints when needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_dsopts = {\n",
    "    'where' : ' Sex = \"F\" ',\n",
    "    'keep'  : ['Age','Sex'],\n",
    "}\n",
    "class_sds = sas.sasdata(table='class', libref='sashelp', dsopts=class_dsopts)\n",
    "print(class_sds.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Line-by-Line Code Explanation**:\n",
    "\n",
    "* Lines 1-4: Create a dictionary object named `class_dsopts` with two key-value pairs. (Both keys are strings, with one key mapping to a string and the other to a list of strings.)\n",
    "\n",
    "* Line 5: Create a file pointer to the SAS dataset sashelp.class (a physical file on disk), but limit consideration to the columns `Age` and `Sex`, and to rows where the column `Sex` has the value `F`. \n",
    "\n",
    "* Line 6: Print the first 5 rows of the result.\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 3.3.1**. True or False: Specifying dataset options while invoking the `sasdata` method alters the content of the underlying physical file on disk.\n",
    "  \n",
    "&nbsp;\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 3.3.2**. Write several lines of Python code to create a file pointer to SAS dataset sashelp.class, but instead drop the variables `Height` and `Weight`, and only retain observations having `Age` greater than or equal to 14."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notes About Example 3.3**:\n",
    "\n",
    "1. Just like the `sasdata2dataframe` method from Exercise 2.2, the `sasdata` has a `dsopts` argument allowing dataset options to be passed to SAS. However, because `sasdata` only creates a file pointer, the dataset options only affect the values returned when the SAS dataset is read from disk. In particular, the SAS dataset itself will not be changed.\n",
    "\n",
    "2. Just like its pandas counterpart, the `head` method returns the first 5 rows a `sasdata` object (or the corresponding dataset in its entirety, if there are 5 or fewer observations). We can also control the number of rows; e.g., `class_sds.head(3)` returns the first 3 rows.\n",
    "\n",
    "3. The `sasdata` version of `head` also has its own parallel version of the pandas `tail` method, which returns the last few observations.\n",
    "\n",
    "4. The `sas` object represents a connection to a SAS session and was created in Exercise 3.1 above.\n",
    "\n",
    "&nbsp;"
   ]
  },
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