DataCamp NB Template.ipynb

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   "source": [
    "## Course\n",
    "\n",
    "- Notebook Author: [Trenton McKinney][1]\n",
    "- Course: **[DataCamp: Course Name][2]**\n",
    " - This [notebook][3] was created as a reproducible reference.\n",
    " - The material is from the course\n",
    "   - The course website uses `scikit-learn v0.19.2`, `pandas v0.19.2`, and `numpy v1.17.4`\n",
    "   - This notebook uses `v0.24.1`, `v1.2.3`, and `v1.19.2` respectively, so there are differences in model performance compared to the course.\n",
    " - I completed the exercises\n",
    " - If you find the content beneficial, consider a [DataCamp Subscription][4].\n",
    " - I added a function (**`create_dir_save_file`**) to automatically download and save the required data (`data/course_name`) and image (`Images/course_name`) files.\n",
    "\n",
    "  [1]: https://trenton3983.github.io/\n",
    "  [2]: https://Course_Link\n",
    "  [3]: https://notebook_github_address\n",
    "  [4]: https://www.datacamp.com/join-me/MjQwNzMyMg=="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Course Description\n",
    "\n",
    "Text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pprint import pprint as pp\n",
    "from itertools import combinations\n",
    "from pathlib import Path\n",
    "import requests\n",
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('max_columns', 200)\n",
    "pd.set_option('max_rows', 300)\n",
    "pd.set_option('display.expand_frame_repr', True)\n",
    "plt.rcParams[\"patch.force_edgecolor\"] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dir_save_file(dir_path: Path, url: str):\n",
    "    \"\"\"\n",
    "    Check if the path exists and create it if it does not.\n",
    "    Check if the file exists and download it if it does not.\n",
    "    \"\"\"\n",
    "    if not dir_path.parents[0].exists():\n",
    "        dir_path.parents[0].mkdir(parents=True)\n",
    "        print(f'Directory Created: {dir_path.parents[0]}')\n",
    "    else:\n",
    "        print('Directory Exists')\n",
    "        \n",
    "    if not dir_path.exists():\n",
    "        r = requests.get(url, allow_redirects=True)\n",
    "        open(dir_path, 'wb').write(r.content)\n",
    "        print(f'File Created: {dir_path.name}')\n",
    "    else:\n",
    "        print('File Exists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = Path('data/...')\n",
    "images_dir = Path('Images/...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets\n",
    "\n",
    "* [dataset][1]\n",
    "\n",
    "  [1]: https://dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'https://...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [file]\n",
    "data_paths = list()\n",
    "\n",
    "for data in datasets:\n",
    "    file_name = data.split('/')[-1].replace('?raw=true', '')\n",
    "    data_path = data_dir / file_name\n",
    "    create_dir_save_file(data_path, data)\n",
    "    data_paths.append(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Memory Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These are the usual ipython objects, including this one you are creating\n",
    "ipython_vars = ['In', 'Out', 'exit', 'quit', 'get_ipython', 'ipython_vars']  # list a variables\n",
    "\n",
    "# Get a sorted list of the objects and their sizes\n",
    "sorted([(x, sys.getsizeof(globals().get(x))) for x in dir() if not x.startswith('_') and x not in sys.modules and x not in ipython_vars], key=lambda x: x[1], reverse=True)[:11]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 1\n",
    "\n",
    "description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 1\n",
    "\n",
    "description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 1\n",
    "\n",
    "description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 1\n",
    "\n",
    "description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsection Video Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Certificate\n",
    "\n",
    "![](https://raw.githubusercontent.com/trenton3983/DataCamp/master/Images/jpd_dir/file.jpg)"
   ]
  }
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