section09_combining_dfs.ipynb

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    "import pandas as pd"
   ]
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    "# Assignment 1: pd.Concat\n",
    "\n",
    "Combine the 2014 and 2015 data you wrote out in the last section into a single dataframe. \n",
    "\n",
    "Then delete the transactions DataFrame (there is a handy base Python keyword for this)."
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    "# Code to read in transactions data if you need to re-create CSV/Excel files from section 8 \n",
    "# NOTE: You won't have the extra columns we created but it won't matter for this assignment\n",
    "\n",
    "# transactions = pd.read_csv(\"../retail/transactions.csv\", parse_dates=[\"date\"])\n",
    "\n",
    "# Excel\n",
    "\n",
    "# with pd.ExcelWriter(\"DataForChandler.xlsx\") as writer:\n",
    "#     for year in range(2013, 2018):\n",
    "#         transactions.loc[transactions[\"date\"].dt.year == year].to_excel(\n",
    "#             writer, sheet_name=str(year)\n",
    "#         )\n",
    "        \n",
    "# CSV  \n",
    "\n",
    "# for year in range(2013, 2018):\n",
    "#     transactions.loc[transactions[\"date\"].dt.year == year].to_csv(\n",
    "#         f\"transactions_{year}.csv\"\n",
    "#     )"
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    "# Assignment 2: Joins\n",
    "\n",
    "Can you join retail.csv with stores.csv? You'll need to read both files in.\n",
    "\n",
    "Once you have that, plot:\n",
    "* Total sales by city, \n",
    "* The sum of sales by “type” over time,\n",
    "* A stacked bar chart with average daily sales by type by month, with “type” as the “stacks”. \n"
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