{ "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 }