Initial commit

.commit_template

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+
+
+# -----------------------------------------------------------------
+# Write your entire commit message above this line.
+# 
+# The first line should be a quick description of your latest progress. 
+# Then leave a blank line. 
+# Then, taking as many lines as you want, reflect on the current state 
+# of your data science project. Write about whatever you want; here are 
+# a few suggestions: 
+# 
+# - Which recent successes are you proud of?
+# - What are you currently stuck on? Are there parts of this project
+#   that you're worried about, or which you don't know how to do?
+# - Has your work sparked any new ideas or interests?
+# - Have you learned any new skills?
+

argument.ipynb

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+{
+ "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": "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": "markdown",
+   "id": "permanent-pollution",
+   "metadata": {},
+   "source": [
+    "# Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "technical-evans",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Include any import statements you will need\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "heated-blade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continental-franklin",
+   "metadata": {},
+   "source": [
+    "**Data Overview**\n",
+    "\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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recognized-positive",
+   "metadata": {},
+   "source": [
+    "## First Research Question: [✏️ PUT YOUR QUESTION 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": "markdown",
+   "id": "portuguese-japan",
+   "metadata": {},
+   "source": [
+    "### Results "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "negative-highlight",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#######################################################################\n",
+    "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \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": "3.9.7"
+  },
+  "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
+}

proposal.md

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+# 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.*
+
+### What specific research questions will you investigate?
+
+*List 2-4 specific research questions. Each should be answerable 
+using your data set.*
+
+## Data source
+
+### What data set will you use to answer your overarching question? 
+
+*Give the title of your data set and provide a link to your data.*
+
+### Where is this data from?
+
+*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?
+
+*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 
+
+### How will you use your data set to answer your quantitative questions?
+
+*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).*

pyproject.toml

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+[tool.poetry]
+name = "project-argument"
+version = "0.1.0"
+description = ""
+authors = ["Chris Proctor <chris@chrisproctor.net>"]
+readme = "README.md"
+packages = [{include = "project_argument"}]
+
+[tool.poetry.dependencies]
+python = "^3.11"
+jupyter = "^1.0.0"
+seaborn = "^0.12.2"
+pandas = "^2.0.3"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"