{
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_FOLDER = 'input/'\n",
    "\n",
    "transactions    = pd.read_csv(os.path.join(DATA_FOLDER, 'sales_train.csv'))\n",
    "items           = pd.read_csv(os.path.join(DATA_FOLDER, 'items.csv'))\n",
    "item_categories = pd.read_csv(os.path.join(DATA_FOLDER, 'item_categories.csv'))\n",
    "shops           = pd.read_csv(os.path.join(DATA_FOLDER, 'shops.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first question is:\n",
    "\n",
    "1. ** What was the maximum total revenue among all the shops in September, 2014?** \n",
    "\n",
    "\n",
    "* Hereinafter *revenue* refers to total sales minus value of goods returned.\n",
    "\n",
    "*Hints:*\n",
    "\n",
    "* Sometimes items are returned, find such examples in the dataset. \n",
    "* It is handy to split `date` field into [`day`, `month`, `year`] components and use `df.year == 14` and `df.month == 9` in order to select target subset of dates.\n",
    "* You may work with `date` feature as with srings, or you may first convert it to `pd.datetime` type with `pd.to_datetime` function, but do not forget to set correct `format` argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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