section05_data_viz.ipynb

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   "cell_type": "code",
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   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2bf3eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "oil = pd.read_csv(\"../retail/oil.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "124fcfe4",
   "metadata": {},
   "source": [
    "# Assignment 1: Simple Line Chart\n",
    "\n",
    "Build a simple line plot for the oil series. Are there any interesting patterns here? \n",
    "\n",
    "What happened in 2014 to oil price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "769d6b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "oil.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18889203",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cba0a52",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24a7c143",
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  },
  {
   "cell_type": "markdown",
   "id": "8583ad04",
   "metadata": {},
   "source": [
    "# Assignment 2: Upgraded Line Chart\n",
    "\n",
    "\n",
    "Give your chart a style, add axis titles, and change the color of the line to black. \n",
    "\n",
    "Consider converting the date column to datetime64.\n",
    "\n",
    "Rename the column to a more intuitive name for the audience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9be2c9d",
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "289b836c",
   "metadata": {},
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95038a31",
   "metadata": {},
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  },
  {
   "cell_type": "markdown",
   "id": "8a2e777e",
   "metadata": {},
   "source": [
    "# Assignment 3: Subplots\n",
    "\n",
    "Filter the transactions table to stores 1, 2, 3 and 4, then reshape it so each row is a date, and the columns of the table are the transactions for each store. \n",
    "\n",
    "Once you've done that, create a 2x2 line plot of transactions vs time, with one plot for each store.\n",
    "\n",
    "Thanks!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30087a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions = pd.read_csv(\"../retail/transactions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5afc381",
   "metadata": {
    "scrolled": true
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   "outputs": [],
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "494b76d1",
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  {
   "cell_type": "markdown",
   "id": "d65bdfed",
   "metadata": {},
   "source": [
    "# Assignment 4: Bar Charts\n",
    "\n",
    "Create a sorted Bar chart of total sales by store number for stores 1, 2, 3 and 4.\n",
    "\n",
    "Then create a horizontal stacked bar chart of sales by month, stacked by store number. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "040cb0eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert dates to datetime64\n",
    "\n",
    "stores_1234.index = stores_1234.index.astype(\"datetime64[ns]\") \n",
    "\n",
    "stores_1234"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec11c9dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# stores_1234.index.month will allow you to groupby the month! You can also slice it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c29d3eb5",
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  {
   "cell_type": "markdown",
   "id": "af4efa8a",
   "metadata": {},
   "source": [
    "# Assignment 5: Scatterplots\n",
    "\n",
    "Plot the relationship between store 2 and store 3 sales.\n",
    "\n",
    "Color the dots by month.\n",
    "\n",
    "Use `c=stores_1234.index.month` to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2acab47b",
   "metadata": {},
   "outputs": [],
   "source": [
    "stores_1234.head()"
   ]
  },
  {
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   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "237fbd80",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d83396b0",
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  {
   "cell_type": "markdown",
   "id": "2218dddb",
   "metadata": {},
   "source": [
    "# Assignment 6: Histograms\n",
    "\n",
    "Hi there,\n",
    "\n",
    "Can you plot the distributions of sales for stores 2 and 3?\n",
    "\n",
    "I want to see how often store 2 has sales similar to store 3, as well as if there are any outlier type days.\n",
    "\n",
    "Thanks!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "884b3fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "stores_1234.head()"
   ]
  },
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