generated from mwc/project_argument
427 lines
11 KiB
Plaintext
427 lines
11 KiB
Plaintext
{
|
|
"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 becuase I generally try to eat healthy. Sometimes quick meals are a necesity. 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": 6,
|
|
"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": 10,
|
|
"id": "overhead-sigma",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
|
|
"\n",
|
|
"# Load the dataset\n",
|
|
"def pandas(pd):\n",
|
|
" file_path = 'Fast_Food_Dataset/nutrition.csv' # Update this with the correct path\n",
|
|
" data = pd.read_csv(file_path)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "heated-blade",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#check first 15 rows\n",
|
|
"def data (read_csv):\n",
|
|
" data.head(15)"
|
|
]
|
|
},
|
|
{
|
|
"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": 15,
|
|
"id": "basic-canadian",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#Import any helper files you need here\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": null,
|
|
"id": "0e3d2271-0361-4d42-a237-eff6b909c7b3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "44d5cdff-d651-46b8-8ee9-6bd6019c96c8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "SyntaxError",
|
|
"evalue": "invalid syntax (339389244.py, line 1)",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;36m Cell \u001b[0;32mIn[16], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m I will use rows 1-15\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"I will use rows 1-15 \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "portuguese-japan",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Results "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "negative-highlight",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'data' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#######################################################################\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m#import rows 1-15\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241m.\u001b[39mhead()\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m### \u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m### Your data analysis may include a statistic and/or a data visualization\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m#######################################################################\u001b[39;00m\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#######################################################################\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": [],
|
|
"source": [
|
|
"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
|
|
]
|
|
},
|
|
{
|
|
"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
|
|
}
|