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I am not done with the argument project, but I've hit a barrier. I keep getting error

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messages. Getting a bit frustrated tonight, so I'm takaing a break.
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Heather
2025-11-03 19:34:47 -05:00
parent 6b66d5d9c7
commit 059fd8fabe
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419
.ipynb_checkpoints/argument-checkpoint.ipynb Normal file
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@@ -0,0 +1,419 @@
{
"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": 12,
"id": "heated-blade",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (1804757086.py, line 1)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Cell \u001b[0;32mIn[12], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m def data (pd.read_csv):\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"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": null,
"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": "44d5cdff-d651-46b8-8ee9-6bd6019c96c8",
"metadata": {},
"outputs": [],
"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",
"#import rows 1-15\n",
"data.head()\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
}

62
.ipynb_checkpoints/proposal-checkpoint.md Normal file
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@@ -0,0 +1,62 @@
# Project proposal
This planning document will also form the introduction of your
argument.
## Overarching Question
### What central question are you interested in exploring? Why are you interested in exploring this question?
*This should be the big picture question that you ask; use at least 5
sentences to describe why you are interested in it.*
My overarching question is whether it is possible to find a healthy option at a fast food place.
By healthy, I will look at how many of the calories are made up of fat. I'm interested in exploring this because I generally
do not eat at fast food places, but, on the rare occasion that I do, I usually just opt for a burger or chicken sandwich.
When I was scrolling through looking for data this caught my eye. The dataset focuses on 6 of the largest and most popular fast food places. I will focus on the first 15 rows to mak it more managable for me.
### What specific research questions will you investigate?
*List 2-4 specific research questions. Each should be answerable
using your data set.*
Question 1 - For the first 15 rows, which food item has the most/least calories
Question 2 - From the first 15 rows, which food has the most/least fat
Question 3- which has the most/least sodium
## Data source
Kaggle.com
### What data set will you use to answer your overarching question?
My dataset is from Kaggle.com and is title nutritional-fast-food-dataset
https://www.kaggle.com/datasets/tan5577/nutritonal-fast-food-dataset/data
*Give the title of your data set and provide a link to your data.*
### Where is this data from?
Data on fast food nutrition was collected from restaurant menus, official nutritional labels, and verified food databases. The information includes calories, macronutrients (carbohydrates, proteins, fats), and micronutrients. Reliable sources and standardized measurement methods were used to ensure accuracy.
*Describe the source of the data set--not just where you downloaded it, but
the person or organization who gathered the data. Explain why you trust them.*
### What is this data about?
I'm focusing on the first 15 rows and columns dealing with total fat, sodium and calories
*Describe the nature of the data in the dataset, including the number of rows
and some of the columns which will be important to you.*
## Methods
I will use the analysis in the dataset to compare and contrast the amount of fat, sodium and calories.
### How will you use your data set to answer your quantitative questions?
For question 1 and 2 I plan on using a chart to help visually compare (in question1 ) which food has the most/least calories and (question 2) which food has the most/ least amount of fat
Question 3 will use a table to help visually compare which foods have th emost/ least sodium
*For each research question, explain what you will do with the data set
to answer the question, and how you will present your answer (e.g. a chart or a table).*

116
argument.ipynb
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@@ -16,6 +16,16 @@
"*✏️ 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",
@@ -32,6 +42,26 @@
"*✏️ 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",
@@ -42,39 +72,44 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"id": "technical-evans",
"metadata": {},
"outputs": [],
"source": [
"#Include any import statements you will need\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
"\n",
"#python\n",
"\n",
"def pandas(pd):\n",
" import pandas as pd\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"id": "overhead-sigma",
"metadata": {},
"outputs": [],
"source": [
"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
"\n",
"file_name = \"YOUR_DATASET_FILE_NAME.csv\"\n",
"dataset_path = \"data/\" + file_name\n",
"\n",
"df = pd.read_csv(dataset_path)"
"# 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": null,
"execution_count": 14,
"id": "heated-blade",
"metadata": {},
"outputs": [],
"source": [
"df.head()"
"#check first 15 rows\n",
"def data (read_csv):\n",
" data.head(15)"
]
},
{
@@ -83,7 +118,7 @@
"metadata": {},
"source": [
"**Data Overview**\n",
"\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.*"
]
},
@@ -97,20 +132,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"id": "basic-canadian",
"metadata": {},
"outputs": [],
"source": [
"#Import any helper files you need here"
]
},
{
"cell_type": "markdown",
"id": "recognized-positive",
"metadata": {},
"source": [
"## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
"#Import any helper files you need here\n"
]
},
{
@@ -135,6 +162,33 @@
"\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",
@@ -145,13 +199,25 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 2,
"id": "negative-highlight",
"metadata": {},
"outputs": [],
"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",
"### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
"\n",
"### \n",
"### Your data analysis may include a statistic and/or a data visualization\n",
"#######################################################################"
@@ -310,7 +376,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
"version": "3.12.3"
},
"toc": {
"base_numbering": 1,