{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example notebook for the %%stata cell magic by the IPyStata package. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author:**   Ties de Kok <tdekok@uw.edu>  \n",
    "**Homepage:**    https://github.com/TiesdeKok/ipystata  \n",
    "**PyPi:** https://pypi.python.org/pypi/ipystata  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note: this example notebook uses the Windows only `Stata Automation` mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See Github for an example notebook that uses the `Stata Batch Mode` (supported for Windows, Mac OS X, and Linux)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "require(['notebook/js/codecell'], function(codecell) {\n",
       "\t\t\t  codecell.CodeCell.options_default.highlight_modes['magic_stata'] = {'reg':[/^%%stata/]} ;\n",
       "\t\t\t  Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n",
       "\t\t\t      Jupyter.notebook.get_cells().map(function(cell){\n",
       "\t\t\t          if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n",
       "\t\t\t  });\n",
       "\t\t\t});"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipystata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: You can ignore the `Javascript error adding output!` warning if it pops up with the newest version of Jupyter Notebook *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure ipystata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure that you have registered your Stata instance. (See GitHub for instructions). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check whether IPyStata is working"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Hello, I am printed by Stata.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "\n",
    "display \"Hello, I am printed by Stata.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some examples based on the Stata 13 manual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataset \"auto.dta\" in Stata return it back to Python as a Pandas dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code cell below runs the Stata command **`sysuse auto.dta`** to load the dataset and returns it back to Python via the **`-o car_df`** argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1978 Automobile Data)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -o car_df\n",
    "sysuse auto.dta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`car_df`** is a regular Pandas dataframe on which Python / Pandas actions can be performed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>make</th>\n",
       "      <th>price</th>\n",
       "      <th>mpg</th>\n",
       "      <th>rep78</th>\n",
       "      <th>headroom</th>\n",
       "      <th>trunk</th>\n",
       "      <th>weight</th>\n",
       "      <th>length</th>\n",
       "      <th>turn</th>\n",
       "      <th>displacement</th>\n",
       "      <th>gear_ratio</th>\n",
       "      <th>foreign</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMC Concord</td>\n",
       "      <td>4099</td>\n",
       "      <td>22</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11</td>\n",
       "      <td>2930</td>\n",
       "      <td>186</td>\n",
       "      <td>40</td>\n",
       "      <td>121</td>\n",
       "      <td>3.58</td>\n",
       "      <td>Domestic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Pacer</td>\n",
       "      <td>4749</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>3350</td>\n",
       "      <td>173</td>\n",
       "      <td>40</td>\n",
       "      <td>258</td>\n",
       "      <td>2.53</td>\n",
       "      <td>Domestic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMC Spirit</td>\n",
       "      <td>3799</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2640</td>\n",
       "      <td>168</td>\n",
       "      <td>35</td>\n",
       "      <td>121</td>\n",
       "      <td>3.08</td>\n",
       "      <td>Domestic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Buick Century</td>\n",
       "      <td>4816</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>16</td>\n",
       "      <td>3250</td>\n",
       "      <td>196</td>\n",
       "      <td>40</td>\n",
       "      <td>196</td>\n",
       "      <td>2.93</td>\n",
       "      <td>Domestic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Buick Electra</td>\n",
       "      <td>7827</td>\n",
       "      <td>15</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20</td>\n",
       "      <td>4080</td>\n",
       "      <td>222</td>\n",
       "      <td>43</td>\n",
       "      <td>350</td>\n",
       "      <td>2.41</td>\n",
       "      <td>Domestic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            make  price  mpg  rep78  headroom  trunk  weight  length  turn  \\\n",
       "0    AMC Concord   4099   22    3.0       2.5     11    2930     186    40   \n",
       "1      AMC Pacer   4749   17    3.0       3.0     11    3350     173    40   \n",
       "2     AMC Spirit   3799   22    NaN       3.0     12    2640     168    35   \n",
       "3  Buick Century   4816   20    3.0       4.5     16    3250     196    40   \n",
       "4  Buick Electra   7827   15    4.0       4.0     20    4080     222    43   \n",
       "\n",
       "   displacement  gear_ratio   foreign  \n",
       "0           121        3.58  Domestic  \n",
       "1           258        2.53  Domestic  \n",
       "2           121        3.08  Domestic  \n",
       "3           196        2.93  Domestic  \n",
       "4           350        2.41  Domestic  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic descriptive statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The argument **`-d or --data`** is used to define which dataframe should be set as dataset in Stata.  \n",
    "In the example below the Stata function **`tabulate`** is used to generate some descriptive statistics for the dataframe **`car_df`**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "car_df.to_stata('D:\\Software\\stata15\\test.df', version=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           |                                        headroom\n",
      "   foreign |       1.5          2        2.5          3        3.5          4        4.5          5 |     Total\n",
      "-----------+----------------------------------------------------------------------------------------+----------\n",
      "  Domestic |         3         10          4          7         13         10          4          1 |        52 \n",
      "   Foreign |         1          3         10          6          2          0          0          0 |        22 \n",
      "-----------+----------------------------------------------------------------------------------------+----------\n",
      "     Total |         4         13         14         13         15         10          4          1 |        74 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -d car_df\n",
    "tabulate foreign headroom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These descriptive statistics can be replicated in Pandas using the **`crosstab`** fuction, see the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>headroom</th>\n",
       "      <th>1.5</th>\n",
       "      <th>2.0</th>\n",
       "      <th>2.5</th>\n",
       "      <th>3.0</th>\n",
       "      <th>3.5</th>\n",
       "      <th>4.0</th>\n",
       "      <th>4.5</th>\n",
       "      <th>5.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foreign</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Domestic</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foreign</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "headroom  1.5  2.0  2.5  3.0  3.5  4.0  4.5  5.0  All\n",
       "foreign                                              \n",
       "Domestic    3   10    4    7   13   10    4    1   52\n",
       "Foreign     1    3   10    6    2    0    0    0   22\n",
       "All         4   13   14   13   15   10    4    1   74"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(car_df['foreign'], car_df['headroom'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stata graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPyStata will automatically check whether there are any new graph generated.  \n",
    "If you want to show multiple graphs, you have to make sure to use the `, name(.., replace)`argument in your Stata code.  \n",
    "**Note: the order is not guaranteed to be the same as the generation order. Recommended to use the `title()` argument when showing multiple graphs.**\n",
    "\n",
    "It is possible to prevent graphs from showing using the `-nogr` or `--nograph` arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(highschool and beyond (200 cases))\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%stata -s graph_session\n",
    "use https://stats.idre.ucla.edu/stat/data/hsb2.dta, clear\n",
    "graph twoway scatter read math, name(a, replace) title(\"Graph a\")\n",
    "\n",
    "graph twoway scatter math science, name(b, replace) title(\"Graph b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Python lists as Stata macros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many situations it is convenient to define values or variable names in a Python list or equivalently in a Stata macro.  \n",
    "The **`-i or --input`** argument makes a Python list available for use in Stata as a local macro.  \n",
    "For example, **`-i main_var`** converts the Python list **`['mpg', 'rep78']`** into the following Stata macro: **``main_var'`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_var = ['mpg', 'rep78']\n",
    "control_var = ['gear_ratio', 'trunk', 'weight', 'displacement']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "mpg rep78\n",
      "\n",
      "gear_ratio trunk weight displacement\n",
      "\n",
      "Linear regression                               Number of obs     =         69\n",
      "                                                F(6, 62)          =       8.60\n",
      "                                                Prob > F          =     0.0000\n",
      "                                                R-squared         =     0.4124\n",
      "                                                Root MSE          =     2338.1\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "         mpg |  -76.95578   84.95038    -0.91   0.369    -246.7692     92.8576\n",
      "       rep78 |   899.0818   299.7541     3.00   0.004      299.882    1498.282\n",
      "  gear_ratio |   1479.744   917.5363     1.61   0.112    -354.3846    3313.873\n",
      "       trunk |  -110.3163   80.16622    -1.38   0.174    -270.5663    49.93365\n",
      "      weight |   1.139509   1.187361     0.96   0.341    -1.233991     3.51301\n",
      "displacement |   17.82274   8.523647     2.09   0.041     .7842094    34.86126\n",
      "       _cons |  -5163.323   4965.389    -1.04   0.302    -15088.99    4762.348\n",
      "------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -i main_var -i control_var -os\n",
    "\n",
    "display \"`main_var'\"\n",
    "display \"`control_var'\"\n",
    "\n",
    "regress price `main_var' `control_var', vce(robust)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modify dataset in Stata and return it to Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible create new variables or modify the existing dataset in Stata and have it returned as a Pandas dataframe.  \n",
    "In the example below the output **`-o car_df`** will overwrite the **`car_df`** previously created.  \n",
    "Note, the argument **`-np or --noprint`** can be used to supress any output below the code cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%stata -o car_df -np\n",
    "generate weight_squared = weight^2\n",
    "generate log_weight = log(weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>make</th>\n",
       "      <th>price</th>\n",
       "      <th>mpg</th>\n",
       "      <th>rep78</th>\n",
       "      <th>headroom</th>\n",
       "      <th>trunk</th>\n",
       "      <th>weight</th>\n",
       "      <th>length</th>\n",
       "      <th>turn</th>\n",
       "      <th>displacement</th>\n",
       "      <th>gear_ratio</th>\n",
       "      <th>foreign</th>\n",
       "      <th>weight_squared</th>\n",
       "      <th>log_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMC Concord</td>\n",
       "      <td>4099</td>\n",
       "      <td>22</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11</td>\n",
       "      <td>2930</td>\n",
       "      <td>186</td>\n",
       "      <td>40</td>\n",
       "      <td>121</td>\n",
       "      <td>3.58</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>8584900.0</td>\n",
       "      <td>7.982758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Pacer</td>\n",
       "      <td>4749</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>3350</td>\n",
       "      <td>173</td>\n",
       "      <td>40</td>\n",
       "      <td>258</td>\n",
       "      <td>2.53</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>11222500.0</td>\n",
       "      <td>8.116715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMC Spirit</td>\n",
       "      <td>3799</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2640</td>\n",
       "      <td>168</td>\n",
       "      <td>35</td>\n",
       "      <td>121</td>\n",
       "      <td>3.08</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>6969600.0</td>\n",
       "      <td>7.878534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          make  price  mpg  rep78  headroom  trunk  weight  length  turn  \\\n",
       "0  AMC Concord   4099   22    3.0       2.5     11    2930     186    40   \n",
       "1    AMC Pacer   4749   17    3.0       3.0     11    3350     173    40   \n",
       "2   AMC Spirit   3799   22    NaN       3.0     12    2640     168    35   \n",
       "\n",
       "   displacement  gear_ratio   foreign  weight_squared  log_weight  \n",
       "0           121        3.58  Domestic       8584900.0    7.982758  \n",
       "1           258        2.53  Domestic      11222500.0    8.116715  \n",
       "2           121        3.08  Domestic       6969600.0    7.878534  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve macro from Stata back into Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **`-gm`** or **`--getmacro`** argument allows a macro to be extracted from a Stata session. The macro will be added to the **`macro_dict`** dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Several (2x) macros have been added to the dictionary: macro_dict\n"
     ]
    }
   ],
   "source": [
    "%%stata -s macro_example -gm macro_1 -gm macro_2\n",
    "local macro_1 one two\n",
    "local macro_2 three four"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'macro_1': ['one', 'two'], 'macro_2': ['three', 'four']}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'two']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_dict['macro_1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Python working directory in Stata "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(r'C:/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set the working directory of Stata to: C:\\\n"
     ]
    }
   ],
   "source": [
    "%%stata -cwd\n",
    "display \"`c(pwd)'\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Mata code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(1978 Automobile Data)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -s mata_session\n",
    "sysuse auto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mata output:\n",
      "\n",
      "                  1              2              3\n",
      "    +----------------------------------------------+\n",
      "  1 |  -220.1648801    43.55851009    10254.94983  |\n",
      "    +----------------------------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata --mata -s mata_session\n",
    "y    = st_data(., \"price\")\n",
    "X    = st_data(., \"mpg trunk\")\n",
    "n    = rows(X)\n",
    "X    = X,J(n,1,1)\n",
    "XpX  = quadcross(X, X)\n",
    "XpXi = invsym(XpX)\n",
    "b    = XpXi*quadcross(X, y)\n",
    "b'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Sessions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPyStata 0.2 introduces the possibility to use many different Stata sessions that by default run in the background.  \n",
    "These sessions are defined using the **`-s`** or **`--session`** arguments. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%stata -s session_1 -np\n",
    "local session Hello I am session 1 and I am persistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%stata -s session_2 -np\n",
    "local session Hello I am session 2 and I am persistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello I am session 1 and I am persistent\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -s session_1\n",
    "display \"`session'\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello I am session 2 and I am persistent\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -s session_2\n",
    "display \"`session'\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session example 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example a logistic regression is performed in one cell and a postestimation (predict) is performed on this regression in the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(1978 Automobile Data)\n",
      "\n",
      "Iteration 0:   log likelihood =  -45.03321  \n",
      "Iteration 1:   log likelihood = -29.238536  \n",
      "Iteration 2:   log likelihood = -27.244139  \n",
      "Iteration 3:   log likelihood = -27.175277  \n",
      "Iteration 4:   log likelihood = -27.175156  \n",
      "Iteration 5:   log likelihood = -27.175156  \n",
      "\n",
      "Logistic regression                             Number of obs     =         74\n",
      "                                                LR chi2(2)        =      35.72\n",
      "                                                Prob > chi2       =     0.0000\n",
      "Log likelihood = -27.175156                     Pseudo R2         =     0.3966\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "     foreign |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "      weight |  -.0039067   .0010116    -3.86   0.000    -.0058894    -.001924\n",
      "         mpg |  -.1685869   .0919175    -1.83   0.067    -.3487418     .011568\n",
      "       _cons |   13.70837   4.518709     3.03   0.002     4.851859    22.56487\n",
      "------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -s auto_session\n",
    "sysuse auto\n",
    "logit foreign weight mpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(option pr assumed; Pr(foreign))\n",
      "\n",
      "    Variable |        Obs        Mean    Std. Dev.       Min        Max\n",
      "-------------+---------------------------------------------------------\n",
      "     probhat |         74    .2972973    .3052979    .000729   .8980594\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -s auto_session\n",
    "predict probhat\n",
    "summarize probhat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session manager tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to avoid using unnecessary system resources several tools and automatic cleanup routines are included."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display all active Stata sessions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following sessions have been found:\n",
      "main [active]\n",
      "graph_session [active]\n",
      "macro_example [active]\n",
      "mata_session [active]\n",
      "session_1 [active]\n",
      "session_2 [active]\n",
      "auto_session [active]\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "sessions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reveal all Stata sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revealed 7 Stata sessions.\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "reveal all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hide all Stata sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 Stata sessions have been hidden.\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "hide all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Close all Stata sessions initiated by IPyStata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following sessions have been closed:\n",
      "main\n",
      "graph_session\n",
      "macro_example\n",
      "mata_session\n",
      "session_1\n",
      "session_2\n",
      "auto_session\n",
      "Terminated unattached Stata session.\n"
     ]
    }
   ],
   "source": [
    "%%stata \n",
    "close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close all Stata sessions (**Warning! This closes <u>all</u> Stata windows**)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Terminated 0 running Stata processes\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "close all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An example case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the variable **`large`** in Python and use it as the dependent variable for a binary choice estimation by Stata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_df['large'] = [1 if x > 3 and y > 200 else 0 for x, y in zip(car_df['headroom'], car_df['length'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>headroom</th>\n",
       "      <th>length</th>\n",
       "      <th>large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.5</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>218</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   headroom  length  large\n",
       "0       2.5     186      0\n",
       "1       3.0     173      0\n",
       "2       3.0     168      0\n",
       "3       4.5     196      0\n",
       "4       4.0     222      1\n",
       "5       4.0     218      1\n",
       "6       3.0     170      0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df[['headroom', 'length', 'large']].head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_var = ['mpg', 'rep78']\n",
    "control_var = ['gear_ratio', 'trunk', 'weight', 'displacement']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iteration 0:   log pseudolikelihood =  -39.60355  \n",
      "Iteration 1:   log pseudolikelihood = -19.307161  \n",
      "Iteration 2:   log pseudolikelihood = -13.526857  \n",
      "Iteration 3:   log pseudolikelihood = -10.999644  \n",
      "Iteration 4:   log pseudolikelihood = -10.726345  \n",
      "Iteration 5:   log pseudolikelihood = -10.723111  \n",
      "Iteration 6:   log pseudolikelihood = -10.723109  \n",
      "Iteration 7:   log pseudolikelihood = -10.723109  \n",
      "\n",
      "Logistic regression                             Number of obs     =         69\n",
      "                                                Wald chi2(6)      =      12.90\n",
      "                                                Prob > chi2       =     0.0446\n",
      "Log pseudolikelihood = -10.723109               Pseudo R2         =     0.7292\n",
      "\n",
      "                                  (Std. Err. adjusted for 69 clusters in make)\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "       large |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "         mpg |  -.5846335   .2941083    -1.99   0.047    -1.161075   -.0081918\n",
      "       rep78 |  -1.298127   1.264918    -1.03   0.305    -3.777322    1.181067\n",
      "  gear_ratio |  -1.331913   3.389448    -0.39   0.694    -7.975109    5.311283\n",
      "       trunk |   1.210178   .4830082     2.51   0.012     .2634991    2.156856\n",
      "      weight |  -.0007284   .0022358    -0.33   0.745    -.0051105    .0036536\n",
      "displacement |    .001631   .0160425     0.10   0.919    -.0298119    .0330738\n",
      "       _cons |  -.2977676    16.7841    -0.02   0.986    -33.19401    32.59847\n",
      "------------------------------------------------------------------------------\n",
      "Note: 8 failures and 0 successes completely determined.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -d car_df -i main_var -i control_var\n",
    "\n",
    "logit large `main_var' `control_var', vce(cluster make)"
   ]
  }
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