generated from mwc/lab_pokemon
resubmission
This commit is contained in:
Aminah
parent
7bc3ddd803
commit
577d4b9bcd
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "worldwide-blood",
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"metadata": {},
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"source": [
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"# Introduction"
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]
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},
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{
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"cell_type": "markdown",
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"id": "understanding-numbers",
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"metadata": {},
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"source": [
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"Black & African American Student Population in the US. Primary School vs Undergratuate College (2022)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "greater-circular",
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"metadata": {},
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"source": [
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"How does the Black and African American student population vary by state, from primary school through the first four years of college? "
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]
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},
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{
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"cell_type": "markdown",
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"id": "appreciated-testimony",
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"metadata": {},
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"source": [
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"Although the National Center for Education Statistics (NCES, 2022) indicates an increasing ethnic diversity across the U.S., my personal experiences as a Black student in primary through graduate schools in Maryland, Georgia, and New York seem inconsistent with this data. This research question aims to help me visualize and synthesize these figures comprehensively."
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]
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},
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{
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"cell_type": "markdown",
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"id": "permanent-pollution",
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"metadata": {},
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"source": [
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"# Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"id": "technical-evans",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Include any import statements you will need\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "overhead-sigma",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"False\n"
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]
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},
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{
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: 'data'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[4], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Print out all the files that exist in the data dir\u001b[39;00m\n\u001b[1;32m 6\u001b[0m data_dir \u001b[38;5;241m=\u001b[39m Path(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.5/Frameworks/Python.framework/Versions/3.12/lib/python3.12/pathlib.py:1056\u001b[0m, in \u001b[0;36mPath.iterdir\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1050\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21miterdir\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1051\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Yield path objects of the directory contents.\u001b[39;00m\n\u001b[1;32m 1052\u001b[0m \n\u001b[1;32m 1053\u001b[0m \u001b[38;5;124;03m The children are yielded in arbitrary order, and the\u001b[39;00m\n\u001b[1;32m 1054\u001b[0m \u001b[38;5;124;03m special entries '.' and '..' are not included.\u001b[39;00m\n\u001b[1;32m 1055\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1056\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlistdir\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 1057\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_child_relpath(name)\n",
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"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data'"
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]
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}
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],
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"source": [
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"from pathlib import Path\n",
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"path = Path(\"data/PopulationPrimaryAA2022.csv\")\n",
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"print(path.exists()) # Check whether this path exists\n",
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"\n",
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"# Print out all the files that exist in the data dir\n",
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"data_dir = Path(\"data\")\n",
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"for filepath in data_dir.iterdir(): \n",
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" print(filepath)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "heated-blade",
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'df' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[34], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mhead())\n",
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"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
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]
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}
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],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "continental-franklin",
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"metadata": {},
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"source": [
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"**Data Overview**\n",
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"\n",
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"*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "infinite-instrument",
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"metadata": {},
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"source": [
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"# Methods and Results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "basic-canadian",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import any helper files you need here"
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]
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},
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{
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"cell_type": "markdown",
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"id": "recognized-positive",
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"metadata": {},
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"source": [
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"## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "graduate-palmer",
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"metadata": {},
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"source": [
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"### Methods"
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]
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},
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{
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"cell_type": "markdown",
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"id": "endless-variation",
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"metadata": {},
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"source": [
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"*Explain how you will approach this research question below. Consider the following:* \n",
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" - *Which aspects of the dataset will you use?* \n",
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" - *How will you reorganize/store the data?* \n",
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" - *What data science tools/functions will you use and why?* \n",
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" \n",
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"✏️ *Write your answer below:*\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "portuguese-japan",
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"metadata": {},
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"source": [
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"### Results "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "negative-highlight",
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"metadata": {},
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"outputs": [],
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"source": [
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"#######################################################################\n",
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"### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
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"### \n",
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"### Your data analysis may include a statistic and/or a data visualization\n",
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"#######################################################################"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "victorian-burning",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
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]
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},
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{
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"cell_type": "markdown",
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"id": "collectible-puppy",
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"metadata": {},
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"source": [
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"## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "demographic-future",
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"metadata": {},
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"source": [
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"### Methods"
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]
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},
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{
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"cell_type": "markdown",
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"id": "incorporate-roller",
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"metadata": {},
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"source": [
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"*Explain how you will approach this research question below. Consider the following:* \n",
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" - *Which aspects of the dataset will you use?* \n",
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" - *How will you reorganize/store the data?* \n",
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" - *What data science tools/functions will you use and why?* \n",
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"\n",
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"✏️ *Write your answer below:*\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "juvenile-creation",
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"metadata": {},
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"source": [
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"### Results "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "pursuant-surrey",
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"metadata": {},
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"outputs": [],
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"source": [
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"#######################################################################\n",
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"### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
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"###\n",
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"### Your data analysis may include a statistic and/or a data visualization\n",
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"#######################################################################"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "located-night",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
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]
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},
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{
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"cell_type": "markdown",
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"id": "infectious-symbol",
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"metadata": {},
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"source": [
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"# Discussion"
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]
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},
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{
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"cell_type": "markdown",
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"id": "furnished-camping",
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"metadata": {
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"code_folding": []
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},
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"source": [
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"## Considerations"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bearing-stadium",
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"metadata": {},
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"source": [
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"*It's important to recognize the limitations of our research.\n",
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"Consider the following:*\n",
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"\n",
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"- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
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"- *What were limitations of your datset?*\n",
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"- *Are there any known biases in the data?*\n",
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"\n",
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"✏️ *Write your answer below:*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "beneficial-invasion",
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"metadata": {},
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"source": [
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"## Summary"
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]
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},
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{
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"cell_type": "markdown",
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"id": "about-raise",
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"metadata": {},
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"source": [
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"*Summarize what you discovered through the research. Consider the following:*\n",
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"\n",
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"- *What did you learn about your media consumption/digital habits?*\n",
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"- *Did the results make sense?*\n",
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"- *What was most surprising?*\n",
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"- *How will this project impact you going forward?*\n",
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"\n",
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"✏️ *Write your answer below:*"
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_json": true,
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"text_representation": {
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"extension": ".Rmd",
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"format_name": "rmarkdown",
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"format_version": "1.2",
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"jupytext_version": "1.9.1"
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}
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.5"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": false,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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