{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hoisting dataframes to the module.\n",
    "\n",
    "Creating modules from data is different than software\n",
    "In practice, I really like to tidy up dataframes with specific features cleaned in each notebook.  This approach avoids long and difficult to debug notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import util; df = util.testing.makeDataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the columns in the namespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "globals().update(dict(df.items()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add dataframe operations to our module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hosting attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with IPython.utils.capture.capture_output(): globals().update({x: getattr(df, x) for x in dir(df)})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ø = __name__ == '__main__'\n",
    "if Ø: from deathbeds import __Dataframes_and_modules"
   ]
  },
  {
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>QyXyHT1cgE</th>\n",
       "      <td>2.395613</td>\n",
       "      <td>0.189797</td>\n",
       "      <td>0.247524</td>\n",
       "      <td>0.756322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CpKDTadz2K</th>\n",
       "      <td>-0.667076</td>\n",
       "      <td>-1.609436</td>\n",
       "      <td>1.048942</td>\n",
       "      <td>-0.031083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "QyXyHT1cgE  2.395613  0.189797  0.247524  0.756322\n",
       "CpKDTadz2K -0.667076 -1.609436  1.048942 -0.031083"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if Ø: assert isinstance(__Dataframes_and_modules, __import__('types').ModuleType)\n",
    "Ø and __Dataframes_and_modules.sample(2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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