{
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   },
   "source": [
    "(c) 2016 - present. Enplus Advisors, Inc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "setup"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.precision', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "setup"
    ]
   },
   "source": [
    "**Data**\n",
    "\n",
    "* `sp5_jan` is SP500 market close prices and trading volume for\n",
    "  January 2015.\n",
    "* `sales` is weekly sales data for Acme Widgets Co. for January\n",
    "  2015 in thousands of widgets sold and \\$ millions in revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "setup"
    ]
   },
   "outputs": [],
   "source": [
    "np.random.seed(100)\n",
    "\n",
    "sp5 = pd.read_csv(\n",
    "    'sp500.csv', parse_dates=['date'], index_col=['date'],\n",
    "    usecols=['date', 'close', 'volume'])\\\n",
    "    .sort_index()\n",
    "\n",
    "sp5_jan = sp5.loc['2015-01', :].copy()\n",
    "sp5_jan['volume'] = sp5_jan['volume'] / 1e6\n",
    "sales = pd.DataFrame({\n",
    "    'date': pd.date_range('2015-01-01', '2015-01-31', freq='W'),\n",
    "})\n",
    "sales['widgets_sold'] = abs(10 * np.random.randn(sales.shape[0])).round()\n",
    "sales['revenue'] = sales.widgets_sold * 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Merge `sp5_jan` with `sales`, filling sales data forward. Save\n",
    "the result as `res_1`. Your result should have the same number of records\n",
    "as `sp5_jan`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Convert the output from the previous exercise to long format with\n",
    "`date` as the ID variable, saving the result as `res_2`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "Convert `res_2` back to wide format using the `pivot` method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "Convert `res_2` back to wide format using the `unstack` method."
   ]
  }
 ],
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