project_argument/argument.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "worldwide-blood",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "understanding-numbers",
   "metadata": {},
   "source": [
    "*✏️ Write 2-3 sentences describing your research.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16a88343-24ed-4a92-ae55-6cfd5d6b0eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I am going to extract the first 15 rows from Fast_Food_dataset and analyze which foods wood be the better options if eating at one of these restaurants"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greater-circular",
   "metadata": {},
   "source": [
    "## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "appreciated-testimony",
   "metadata": {},
   "source": [
    "*✏️ Write 2-3 sentences explaining why this question.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c1d1224-0292-4205-800f-ce0c75316075",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Overarching Question: Are there healthy options at fast food restaurants?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37ad7c41-817d-47ce-9aeb-b3b58b5e7761",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I chose this question because I generally try to eat healthy. Sometimes quick meals are a necessity. I am curious if common fast food restaurants truly offer healthier options "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "permanent-pollution",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "technical-evans",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Include any import statements you will need\n",
    "\n",
    "#python\n",
    "\n",
    "def pandas(pd):\n",
    "    import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "overhead-sigma",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
    "\n",
    "# Load the dataset\n",
    "def pandas(pd):\n",
    "     df = pd.read_csv('Fast_Food_Dataset/nutrition.csv')\n",
    "\n",
    "   \n",
    "\n",
    "    # Update this with the correct path\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "heated-blade",
   "metadata": {},
   "outputs": [],
   "source": [
    "#check first 15 rows\n",
    "def data (read_csv):\n",
    "    data.head(15)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continental-franklin",
   "metadata": {},
   "source": [
    "**Data Overview**\n",
    "This dataset is showing the first 15 rows of Fast_Food_Dataset. It comes from Kaggle.com and contains nutritional information from fast food restaurants.\n",
    "*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infinite-instrument",
   "metadata": {},
   "source": [
    "# Methods and Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "basic-canadian",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import any helper files you need here\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.artist as martist\n",
    "import matplotlib.patches as mpatches\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graduate-palmer",
   "metadata": {},
   "source": [
    "### Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "endless-variation",
   "metadata": {},
   "source": [
    "*Explain how you will approach this research question below. Consider the following:* \n",
    "  - *Which aspects of the dataset will you use?* \n",
    "  - *How will you reorganize/store the data?* \n",
    "  - *What data science tools/functions will you use and why?* \n",
    "  \n",
    "✏️ *Write your answer below:*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e3d2271-0361-4d42-a237-eff6b909c7b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I will focus on rows 1-15 to reorganize the data into how many calories come from fat, which has the least amout of sodium and least amout of calories"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-japan",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "negative-highlight",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################################################\n",
    "\n",
    "# Calculate calories from fat (assuming fat column is in grams)\n",
    "# Formula: fat_grams * 9 calories per gram\n",
    "def first_15():\n",
    "    first_15['fat_calories']= first_15['fat'] * 9\n",
    "\n",
    "### \n",
    "### Your data analysis may include a statistic and/or a data visualization\n",
    "#######################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "victorian-burning",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2142274504.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[16], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m    def first_15('item', 'fat', 'fat_calories', 'calories'):\u001b[0m\n\u001b[0m                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# Display relevant columns\n",
    "def first_15('item', 'fat', 'fat_calories', 'calories'):\n",
    "    return first_15"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collectible-puppy",
   "metadata": {},
   "source": [
    "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "demographic-future",
   "metadata": {},
   "source": [
    "### Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorporate-roller",
   "metadata": {},
   "source": [
    "*Explain how you will approach this research question below. Consider the following:* \n",
    "  - *Which aspects of the dataset will you use?* \n",
    "  - *How will you reorganize/store the data?* \n",
    "  - *What data science tools/functions will you use and why?* \n",
    "\n",
    "✏️ *Write your answer below:*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "juvenile-creation",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "pursuant-surrey",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################################################\n",
    "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
    "###\n",
    "### Your data analysis may include a statistic and/or a data visualization\n",
    "#######################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "located-night",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infectious-symbol",
   "metadata": {},
   "source": [
    "# Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "furnished-camping",
   "metadata": {
    "code_folding": []
   },
   "source": [
    "## Considerations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bearing-stadium",
   "metadata": {},
   "source": [
    "*It's important to recognize the limitations of our research.\n",
    "Consider the following:*\n",
    "\n",
    "- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
    "- *What were limitations of your datset?*\n",
    "- *Are there any known biases in the data?*\n",
    "\n",
    "✏️ *Write your answer below:*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beneficial-invasion",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "about-raise",
   "metadata": {},
   "source": [
    "*Summarize what you discovered through the research. Consider the following:*\n",
    "\n",
    "- *What did you learn about your media consumption/digital habits?*\n",
    "- *Did the results make sense?*\n",
    "- *What was most surprising?*\n",
    "- *How will this project impact you going forward?*\n",
    "\n",
    "✏️ *Write your answer below:*"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_json": true,
   "text_representation": {
    "extension": ".Rmd",
    "format_name": "rmarkdown",
    "format_version": "1.2",
    "jupytext_version": "1.9.1"
   }
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}