FinalProjectGroup082-Fa21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project (Homelessness and Academic Performance in Students)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [X] YES - make available\n",
    "* [  ] NO - keep private"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our project, we aimed to analyze the difference in graduation rates and academic performance in standardized tests between homeless and non-homeless students across the United States. Our initial research suggested that homeless students were underachieving academically and had higher drop out rates than their non-homeless counterparts. In our results, our initial research was proven to be correct, we found that homeless student did in fact have lower graduation dates and lower proficiency in standardized tests when compared to their non-homeless peers. Regionally, all states demonstrated non-homeless students to outperform their homeless peers, the degree of which varied randomly across states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Christopher Yoon\n",
    "- Uday Mehra\n",
    "- Ana Cunha\n",
    "- Albert Aung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does state (i.e. the state of California in United States) affect the difference in graduation rates and academic performance in state tests (subjects math and reading/language to be specific) betwen homeless and non-homeless students?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's no surprise that being homeless generally hampers a student's ability to perform in school. With the rates of homelessness steadily rising throughout the years and spiking during the pandemic, our group was interested on how we can quantify the discrepancy in the education of homeless and non-homeless students. \n",
    "\n",
    "After doing some initial research, we've found that overall, homeless students struggle to stay in school and greatly underachieve academically in comparison to their non-homeless counterparts. The bulk of our initial research is based in the findings of the National Center for Homeless Education. This organization releases yearly summaries on the education of homeless children. Their data is directly submitted from states through their \"Consolidated State Performance Reports (CSPR)\" and is directed for the Education for Homeless Children and Youth. A key limitation to their data is that it is only reporting on the homeless of children and youth who are enrolled in public districts. Nevertheless, their analysis in the 2018-2019 school year quantified academic achievement through the children's performance in state-given standardized tests. They simply stated the percentage of students who received a \"proficient\" or \"valid\" score in science, math, language arts, and overall score by state. Then compared those scores to those of non-homeless students is economically disadvantaged areas. They also reported the graduation rates of different categories of homeless students but didn't compare that to those of non-homeless students. Overall, they learned that homeless students, on average, scored 8-9% lower than economically disadvantaged students statewide. \n",
    "\n",
    "In another study conducted by the Institute for Children, Poverty & Homelessness, they looked at how school absences in homeless students in New York City affected their overall academic performance. They never really cite where they got their data from, but they do mention the \"New York City Department of Education\" in their analysis, so we assumed this was all publicly available data. The data found that homeless students were more likely to be chronic absentees. They also found that some of the reason why homeless students were chronic absentees were because homeless students generally transferred schools more often than those who were not homeless. After this, they showed how absenteeism negatively affected the state test pass rate and grade retention for students. Altogether, they learned that chronically absent homeless students are much more likely to fail their 3rd-8th grade State tests and be held back whereas homeless students who are not chronically absent are more likely to perform just as well as their housed counterparts. \n",
    "\n",
    "The comparative study conducted by Rubin, Erickson, San Agustin, Cleary, Allen and Cohen explored the cognitive and academic statues for children aged 6 to 11 year olds since they found out that across the United States every region has increases in homelessness but there were no research pointing out the academic and cognitive abiliies of the homeless children. From August 1990 to 1992, they compared the standard cognitive and academic performance of 102 homeless children in New York City to 178 housed group of children. In the results, they were able to find that verbal and non-verbal intelligence were \"not significant different\" between the two tested groups. However, in academic achievement homeless children performed worse in reading, spelling and arithmetic with reasons being noted to \"the number of school changes\" and \"grade repetition\". They learned from the study that in terms of cognitive ability and performance the children were not all different but in terms of academic achievement, there seems to be significantly lesser performance from the homeless children compared to non-homeless children.\n",
    "\n",
    "In the article by The National School Boards Association, Homeless Students in Public Schools America: Down but Not Out, questions such as \"Who are homeless students?\", \"How many homeless students attend public schools?\", \"Where do homeless students live and go to school?\", \"What are some characteristics of homeless students?\", \"How do homeless students perform academically?\" and \"What are some funding sources to support homeless students?\" are mentioned and discussed in detail. What caught our eye here the statistics on homeless students facing additional difficulties such as how approximately 18% of homeless students also sheltered disabilities and that 16% of them were English learners or people who has English as a second language. Around 10% of the stduents were \"unacommpanied homeless youth or youth who were not in physical custody of a parent or guardian.\" More related to our research was how they performed academically. It is pointed out in the article that only about 64% of homeless students graduate from high school compared to 78% of low-income studetns and 84% comparing to all students. It was also pointed how \"homelessness is a serious negative factor that affects student achievement.\" A 2019 report about homeless students in New York City show that homeless students performed at half the proficiency housed students do interms of their Math and English Languatge Art assessment in 2016-17 academic school year.\n",
    "\n",
    "In Exploring the Academic and Social Experiences of Homeless College Students by Digital Commons @ the University of Nebraska- Lincoln, students were interviewed through semi-structured interviews and it was apparent from the interviews that four themes emerged: hidden identity, students had few to zero support systems that offered positive encouragement, the varying living situations of each individual participant and students were determined to continue their education which gave them hope. In more detail, hidden identity talked about how students kept the fact that they were homeless a secret since they would be given negative or differential treatment from other people as well as interacting with people within their institutions. The second, which was support was that family was really the only place most of them had support from as they have few to none support from friends or institutions with some only having themselves to care for them. The third was how different options and routes they had to turn to for housing and how each individual's experience was different from the other. And the last being that education gave them hope and allowed them the necessary motivation and platform for them to go attain some of their goals and make life easier for them and how they can use it to help not only themselves but others as well. \n",
    "\n",
    "\n",
    "\n",
    "References: \n",
    "- 1) https://nche.ed.gov/wp-content/uploads/2021/04/Federal-Data-Summary-SY-16.17-to-18.19-Final.pdf\n",
    "- 2) https://www.icphusa.org/wp-content/uploads/2016/09/ICPH-Policy-Report_Empty-Seats_Chronic-Absenteeism.pdf\n",
    "- 3) https://pubmed.ncbi.nlm.nih.gov/8604259/\n",
    "- 4) https://www.nsba.org/Perspectives/2021/homeless-students\n",
    "- 5) https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1239&context=cehsedaddiss\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict that across all the states, non-homeless students would perform better in terms of graduation rate and academic performance compared to homeless students. This comes from non-homeless students receiving more support, opportunities and resources in terms of their academic pursuit compared to homeless students. Homeless students also have more to worry about on their plates and this disadvantage with many others may lead towards a negative downturn in their academic performances and graduation rates. Our background research also seems to point itself out in this direction. In terms of whether location (i.e. state) plays a role in the graduation and academic performance of homeless students compared to non-homeless, we do not see that there would be any significant differences as a lot of homeless students would face similar situations wherever they live albeit a few different laws and such pertaining to them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset Name: SY 2018-19 Adjusted Cohort Graduation Rate New!\n",
    "- Link to the dataset: https://www2.ed.gov/about/inits/ed/edfacts/data-files/index.html\n",
    "- Number of observations: 22900 \n",
    "- Description: This is a dataset containing high school graduation rates across states in the U.S. for the 2018-2019 academic year.\n",
    "\n",
    "Dataset Name: SY 2018-19 Achievement Results for State Assessments in Reading/Language Arts New!\n",
    "- Link to the dataset: https://www2.ed.gov/about/inits/ed/edfacts/data-files/index.html\n",
    "- Number of observations: 89,916\n",
    "- Description: This is a dataset containing the performance of students from elementary to high school on state-specific standardized testing in Reading and/or Language Arts. Achievement data is reported in subgroups, some of them being, race, sex, and homeless enrolled status.\n",
    "\n",
    "Dataset Name: SY 2018-19 Achievement Results for State Assessments in Mathematics New!\n",
    "- Link to the dataset: https://www2.ed.gov/about/inits/ed/edfacts/data-files/index.html \n",
    "- Number of observations: 90278\n",
    "- Description: This is a dataset containing the performance of students from elementary to high school on state-specific standardized testing in Mathemathics. Achievement data is reported in subgroups, some of them being, race, sex, and homeless enrolled status. \n",
    "\n",
    "Dataset Name: Cartographic Boundary File - State 2018 Shapefile\n",
    "- Link to the dataset: https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html\n",
    "- Number of observations: 56\n",
    "- Description: This dataset contains the geometries of all the US's states and territories in 2018. NOTE: I converted this data set into a geographic json format using https://mygeodata.cloud/converter/ so that I could import the cartographic boundary file with state names. The \"mygeodata_merged\" is the json file exported from the mygeodata website. \n",
    "\n",
    "We're not necessarily planning to \"combine\" the graduation and achievement data sets in a formal way. We are using graduation rates and academic achievement results as two different metrics for measuring \"academic success\". We will use to the data sets independently to compare how different the homeless and non-homeless populations were between the two data sets. \n",
    "\n",
    "However, we will combine the graduation, math achievement, and rla achievement data sets with the cartographic data set in order to create GeoDataFrames and plot a choropleth using Geopandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Chris Yoon\\Documents\\cogs108\\COGSgroup\\Group082-Fa21\\.venv\\lib\\site-packages\\geopandas\\_compat.py:111: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "from geopandas import GeoSeries \n",
    "\n",
    "#Graph \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns \n",
    "\n",
    "#Stats\n",
    "import statsmodels.api as sm \n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, ks_2samp\n",
    "\n",
    "#Math \n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data generally consists of two parts: graduation rate data and assessment achievement result data. Both these datasets come from the same source, and are therefore similarly broken down. The specific sets we are interested in are broken down by school and should theoretically contain every school in the country, labelled by state. The columns generally start with a top-level statistic (e.g. overall graduation rate), and proceed to go into detail (i.e. graduation rate broken down by demographic).\n",
    "\n",
    "There is lengthy documentation that goes with each CSV file that explains their details. For example, there are many cells that contain NaN values or are blank. Cases where data for that particular combination of conditions was not available (e.g. a school may simply not have enough of a certain demographic to report useful data) are marked as NaN or n/a. Cases where there actually was no data reported at all are left blank. For our purposes, however, both these cases are virtually the same since we are interested in comparing percent rates—having inconsistencies would skew the data, so we simply choose to remove these rows from the datasets.\n",
    "\n",
    "There are also cells marked ‘PS’. These are cases where data was available, but due to the extremely small number of reported datapoints in the category, it would be easy to personally identify students; these scores are overwritten by PS to protect student privacy. Again, for our purposes, it makes sense to just remove these rows.\n",
    "\n",
    "Additionally, there are cells marked ‘GE’, ‘LE’, ‘GT’, and ‘LT’ followed by a number. These are also cases where student privacy is being protected due to low numbers, but instead of completely obfuscating the datapoint, an approximate range has been given instead. The four abbreviations stand for ‘Greater than or equal to’, ‘Less than or equal to’, ‘Greater than’, and ‘Less than’ respectively—a school’s scores for a particular demographic could be reported as being GT80, meaning that greater than 80% of students in that category achieved proficient scores. For these datapoints, we are choosing to make a conservative estimate (and we realize this will mean our analysis will be based on an approximation, but we think the error in the positive and negative directions should more or less average out) by simply picking the number from the term (i.e. GE80 becomes 80). This is close enough that we can conduct a useful analysis.\n",
    "\n",
    "Further, we are extracting only the columns that are pertinent to our analysis. Since we are focused on homelessness and economic disadvantage, those are the columns we will be selecting from the datasets.\n",
    "\n",
    "Lastly, there are cases where values are presented as ranges (i.e. 70-75)—we have chosen to simply take the mean for these values (another conservative estimate that should not effect the overall trends present in the data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Utility function for cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code: \n",
    "- Converts all numerical strings into int 64's\n",
    "- replaces all the comparison operator values: 'GE', 'LE', etc with NaN \n",
    "- finds the average of ranged values (i.e \"40-46\" = 48)\n",
    "- replaces all other non numerical strings with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to turn strings into usable numbers \n",
    "def clean(string): \n",
    "    try: \n",
    "        # Try to convert the passed in string to an int 64\n",
    "        return np.int64(string) \n",
    "    except ValueError: \n",
    "        #Find the average of the ranged values \n",
    "        if '-' in string: \n",
    "            num1, num2 = string.split('-')\n",
    "            num1 = np.int64(num1) \n",
    "            num2 = np.int64(num2) \n",
    "            string = np.int64((num1 + num2) / 2)\n",
    "            return string\n",
    "\n",
    "        # Replace the comparison operators\n",
    "        if ('GE' in string) | ('LE' in string) | ('GT' in string) | ('LT' in string):\n",
    "            return float('nan')\n",
    "\n",
    "        #If there's anything else that's a string, just return NaN\n",
    "        return float('nan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graduation Rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2018-2019 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This cell first imports the csv into a dataframe. It then pulls the columns that describes the state, total number of students that graduated (ALL_COHORT_1819), the total rate of graduation (ALL_RATE_1819), total number of homeless students that graduated (HOM_COHORT_1819), and the rate at which the homeless students graduated (HOM_RATE_1819) in that particular school. It then gets rid of rows with non-values that were inserted by the creators of the dataset for privacy reasons. Afterwards, it utilizes the converter function to convert the columns into usable data. It only runs it on the 'ALL_RATE_1819', 'HOM_COHORT_1819', and 'HOM_RATE_1819' rows because these are the only ones with the values described in the cleaner function's description. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "#Read csv\n",
    "gradRates_1819 = pd.read_csv('data/gradRates/gr2018-2019.csv')\n",
    "\n",
    "#Grab the columns described above\n",
    "gradRates_1819 = gradRates_1819[['STNAM','ALL_COHORT_1819','ALL_RATE_1819', 'HOM_COHORT_1819', 'HOM_RATE_1819']]\n",
    "\n",
    "# Get rid of the values inserted for privacy (see data section for more details)\n",
    "nonsense = ['', 'PS', '.']\n",
    "gradRates_1819.replace(nonsense, float(\"NaN\"), inplace=True)\n",
    "gradRates_1819.dropna(inplace=True)\n",
    "\n",
    "# Use the cleaner function explained at the start to clean columns that need it\n",
    "colToClean = ['ALL_RATE_1819', 'HOM_COHORT_1819', 'HOM_RATE_1819']\n",
    "for col in colToClean: \n",
    "    gradRates_1819[col] = gradRates_1819[col].apply(clean)\n",
    "\n",
    "# Convert the numerical columns to int64's. \n",
    "gradRates_1819.dropna(inplace=True)\n",
    "for col in colToClean: \n",
    "    gradRates_1819[col] = gradRates_1819[col].astype(np.int64)\n",
    "\n",
    "#Check if any empty, 'PS', or NaN values exist in the df\n",
    "print(('PS' in gradRates_1819) | ('' in gradRates_1819) | (gradRates_1819.isna().values.any()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell just gives us a rundown of the cleaned dataset so far. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STNAM              object\n",
      "ALL_COHORT_1819     int64\n",
      "ALL_RATE_1819       int64\n",
      "HOM_COHORT_1819     int64\n",
      "HOM_RATE_1819       int64\n",
      "dtype: object\n",
      "(1610, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_COHORT_1819</th>\n",
       "      <th>ALL_RATE_1819</th>\n",
       "      <th>HOM_COHORT_1819</th>\n",
       "      <th>HOM_RATE_1819</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1610.000000</td>\n",
       "      <td>1610.000000</td>\n",
       "      <td>1610.000000</td>\n",
       "      <td>1610.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>372.055901</td>\n",
       "      <td>75.669565</td>\n",
       "      <td>36.280124</td>\n",
       "      <td>61.753416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>236.277742</td>\n",
       "      <td>18.792614</td>\n",
       "      <td>30.665365</td>\n",
       "      <td>17.366540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>199.250000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>347.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>490.750000</td>\n",
       "      <td>88.750000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3016.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ALL_COHORT_1819  ALL_RATE_1819  HOM_COHORT_1819  HOM_RATE_1819\n",
       "count      1610.000000    1610.000000      1610.000000    1610.000000\n",
       "mean        372.055901      75.669565        36.280124      61.753416\n",
       "std         236.277742      18.792614        30.665365      17.366540\n",
       "min          24.000000       5.000000        16.000000       7.000000\n",
       "25%         199.250000      69.000000        20.000000      49.000000\n",
       "50%         347.000000      82.000000        27.000000      69.000000\n",
       "75%         490.750000      88.750000        41.000000      69.000000\n",
       "max        3016.000000      98.000000       549.000000      97.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(gradRates_1819.dtypes)\n",
    "print(gradRates_1819.shape)\n",
    "gradRates_1819.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the data set now has our desired types and has shrunk considerably. Just based on quick glance of the All_rate and HOM_rate, we see that the HOM_rate is lower. \n",
    "\n",
    "Right now, the \"all\" student category includes homeless grad rates. For the sake of a better comparison, we're going to make two new columns: the number of non-homeless students that graduated (NOTHOM_cohort) and the rate of non-homeless students that graduated (NOTHOM_rate). This can be done by first finding the total number of non-homeless students that graduated which is simply the total number of students that graduated (ALL_COHORT) subtracted by the total number of homeless students that graduated (HOM_COHORT). Then we find the total number of students recorded in a given row by dividing the total number of students that graduated (ALL_COHORT) by the total graduation rate ('ALL_RATE'). Using the same logic, we find the total number of homeless students and then subtract the total number of students by the total number of homeless students to get the total number of non-homeless students. Then we find the rate of non homeless graduation by dividing the total number of non-homeless students that graduated from the total number of non-homeless students. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAM</th>\n",
       "      <th>ALL_COHORT_1819</th>\n",
       "      <th>ALL_RATE_1819</th>\n",
       "      <th>NOTHOM_cohort</th>\n",
       "      <th>NOTHOM_rate</th>\n",
       "      <th>HOM_COHORT_1819</th>\n",
       "      <th>HOM_RATE_1819</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>301</td>\n",
       "      <td>83</td>\n",
       "      <td>283</td>\n",
       "      <td>84</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>139</td>\n",
       "      <td>87</td>\n",
       "      <td>105</td>\n",
       "      <td>88</td>\n",
       "      <td>34</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>688</td>\n",
       "      <td>94</td>\n",
       "      <td>625</td>\n",
       "      <td>95</td>\n",
       "      <td>63</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>373</td>\n",
       "      <td>87</td>\n",
       "      <td>344</td>\n",
       "      <td>89</td>\n",
       "      <td>29</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>491</td>\n",
       "      <td>88</td>\n",
       "      <td>435</td>\n",
       "      <td>90</td>\n",
       "      <td>56</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STNAM  ALL_COHORT_1819  ALL_RATE_1819  NOTHOM_cohort  NOTHOM_rate  \\\n",
       "133  ALABAMA              301             83            283           84   \n",
       "152  ALABAMA              139             87            105           88   \n",
       "259  ALABAMA              688             94            625           95   \n",
       "262  ALABAMA              373             87            344           89   \n",
       "263  ALABAMA              491             88            435           90   \n",
       "\n",
       "     HOM_COHORT_1819  HOM_RATE_1819  \n",
       "133               18             69  \n",
       "152               34             84  \n",
       "259               63             87  \n",
       "262               29             69  \n",
       "263               56             74  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradRates_1819['NOTHOM_cohort'] = gradRates_1819['ALL_COHORT_1819'] - gradRates_1819['HOM_COHORT_1819']\n",
    "\n",
    "# Calculate the number of total non-homeless students \n",
    "gradRates_1819['NOTHOM_total'] = np.int64(np.round((gradRates_1819['ALL_COHORT_1819'] / (gradRates_1819['ALL_RATE_1819'] / 100)) - (gradRates_1819['HOM_COHORT_1819'] / (gradRates_1819['HOM_RATE_1819'] / 100)), 0))\n",
    "gradRates_1819['NOTHOM_rate'] = np.int64(np.round((gradRates_1819['NOTHOM_cohort'] / gradRates_1819['NOTHOM_total']) * 100, 0))\n",
    "\n",
    "\n",
    "#Cut out rows where rounding errors caused the not-homeless cohort rate equal a negative number \n",
    "gradRates_1819 = gradRates_1819[gradRates_1819['NOTHOM_rate'] > 0]\n",
    "\n",
    "#Get rid of temp column \n",
    "gradRates_1819.pop('NOTHOM_total')\n",
    "\n",
    "# Rearrange the columns \n",
    "col = gradRates_1819.pop('NOTHOM_cohort')\n",
    "gradRates_1819.insert(3, col.name, col)\n",
    "col = gradRates_1819.pop('NOTHOM_rate')\n",
    "gradRates_1819.insert(4, col.name, col)\n",
    "\n",
    "gradRates_1819.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the columns so they're easier to work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAM</th>\n",
       "      <th>ALL_cohort</th>\n",
       "      <th>ALL_rate</th>\n",
       "      <th>NOTHOM_cohort</th>\n",
       "      <th>NOTHOM_rate</th>\n",
       "      <th>HOM_cohort</th>\n",
       "      <th>HOM_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>301</td>\n",
       "      <td>83</td>\n",
       "      <td>283</td>\n",
       "      <td>84</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>139</td>\n",
       "      <td>87</td>\n",
       "      <td>105</td>\n",
       "      <td>88</td>\n",
       "      <td>34</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>688</td>\n",
       "      <td>94</td>\n",
       "      <td>625</td>\n",
       "      <td>95</td>\n",
       "      <td>63</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>373</td>\n",
       "      <td>87</td>\n",
       "      <td>344</td>\n",
       "      <td>89</td>\n",
       "      <td>29</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>491</td>\n",
       "      <td>88</td>\n",
       "      <td>435</td>\n",
       "      <td>90</td>\n",
       "      <td>56</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STNAM  ALL_cohort  ALL_rate  NOTHOM_cohort  NOTHOM_rate  HOM_cohort  \\\n",
       "133  ALABAMA         301        83            283           84          18   \n",
       "152  ALABAMA         139        87            105           88          34   \n",
       "259  ALABAMA         688        94            625           95          63   \n",
       "262  ALABAMA         373        87            344           89          29   \n",
       "263  ALABAMA         491        88            435           90          56   \n",
       "\n",
       "     HOM_rate  \n",
       "133        69  \n",
       "152        84  \n",
       "259        87  \n",
       "262        69  \n",
       "263        74  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradRates_1819 = gradRates_1819.rename(columns={\n",
    "    \"ALL_COHORT_1819\": \"ALL_cohort\", \n",
    "    \"ALL_RATE_1819\" : \"ALL_rate\", \n",
    "    \"HOM_COHORT_1819\": \"HOM_cohort\", \n",
    "    \"HOM_RATE_1819\" : \"HOM_rate\"})\n",
    "gradRates_1819.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, take a look at the dataframe with the non homeless student graduation data added in. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_cohort</th>\n",
       "      <th>ALL_rate</th>\n",
       "      <th>NOTHOM_cohort</th>\n",
       "      <th>NOTHOM_rate</th>\n",
       "      <th>HOM_cohort</th>\n",
       "      <th>HOM_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1609.000000</td>\n",
       "      <td>1609.000000</td>\n",
       "      <td>1609.000000</td>\n",
       "      <td>1609.000000</td>\n",
       "      <td>1609.000000</td>\n",
       "      <td>1609.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>372.272219</td>\n",
       "      <td>75.686140</td>\n",
       "      <td>335.984462</td>\n",
       "      <td>77.998135</td>\n",
       "      <td>36.287756</td>\n",
       "      <td>61.761342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>236.191671</td>\n",
       "      <td>18.786681</td>\n",
       "      <td>228.895293</td>\n",
       "      <td>19.395161</td>\n",
       "      <td>30.673369</td>\n",
       "      <td>17.369026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>347.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>491.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3016.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2999.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ALL_cohort     ALL_rate  NOTHOM_cohort  NOTHOM_rate   HOM_cohort  \\\n",
       "count  1609.000000  1609.000000    1609.000000  1609.000000  1609.000000   \n",
       "mean    372.272219    75.686140     335.984462    77.998135    36.287756   \n",
       "std     236.191671    18.786681     228.895293    19.395161    30.673369   \n",
       "min      39.000000     5.000000       5.000000     5.000000    16.000000   \n",
       "25%     200.000000    69.000000     169.000000    72.000000    20.000000   \n",
       "50%     347.000000    82.000000     313.000000    85.000000    27.000000   \n",
       "75%     491.000000    89.000000     450.000000    91.000000    41.000000   \n",
       "max    3016.000000    98.000000    2999.000000   188.000000   549.000000   \n",
       "\n",
       "          HOM_rate  \n",
       "count  1609.000000  \n",
       "mean     61.761342  \n",
       "std      17.369026  \n",
       "min       7.000000  \n",
       "25%      49.000000  \n",
       "50%      69.000000  \n",
       "75%      69.000000  \n",
       "max      97.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradRates_1819.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The maximum of the rates should be 100 because they are percentages. Clearly, something is wrong with NOTHOM_rate. Let's check out all rows that are >= 100 because they most likely have some sort of rounding error associated with them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAM</th>\n",
       "      <th>ALL_cohort</th>\n",
       "      <th>ALL_rate</th>\n",
       "      <th>NOTHOM_cohort</th>\n",
       "      <th>NOTHOM_rate</th>\n",
       "      <th>HOM_cohort</th>\n",
       "      <th>HOM_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2644</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>607</td>\n",
       "      <td>97</td>\n",
       "      <td>589</td>\n",
       "      <td>100</td>\n",
       "      <td>18</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3778</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>39</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>123</td>\n",
       "      <td>23</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4116</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>15</td>\n",
       "      <td>188</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11947</th>\n",
       "      <td>MISSOURI</td>\n",
       "      <td>290</td>\n",
       "      <td>97</td>\n",
       "      <td>272</td>\n",
       "      <td>100</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19737</th>\n",
       "      <td>TEXAS</td>\n",
       "      <td>451</td>\n",
       "      <td>97</td>\n",
       "      <td>424</td>\n",
       "      <td>100</td>\n",
       "      <td>27</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21301</th>\n",
       "      <td>VIRGINIA</td>\n",
       "      <td>372</td>\n",
       "      <td>73</td>\n",
       "      <td>316</td>\n",
       "      <td>114</td>\n",
       "      <td>56</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22064</th>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>145</td>\n",
       "      <td>87</td>\n",
       "      <td>122</td>\n",
       "      <td>102</td>\n",
       "      <td>23</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            STNAM  ALL_cohort  ALL_rate  NOTHOM_cohort  NOTHOM_rate  \\\n",
       "2644   CALIFORNIA         607        97            589          100   \n",
       "3778   CALIFORNIA          39        84             16          123   \n",
       "4116   CALIFORNIA          40        44             15          188   \n",
       "11947    MISSOURI         290        97            272          100   \n",
       "19737       TEXAS         451        97            424          100   \n",
       "21301    VIRGINIA         372        73            316          114   \n",
       "22064  WASHINGTON         145        87            122          102   \n",
       "\n",
       "       HOM_cohort  HOM_rate  \n",
       "2644           18        49  \n",
       "3778           23        69  \n",
       "4116           25        30  \n",
       "11947          18        69  \n",
       "19737          27        69  \n",
       "21301          56        24  \n",
       "22064          23        49  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradRates_1819[gradRates_1819['NOTHOM_rate'] >= 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After hand calculating to ensure that our formulas were valid, we determined that some of these errors were not solely due to rounding error. Some of the data here simply does not make sense. Row 4116 for example, would've had a student body consisting of ~91 total children and ~83 homeless children. Even after rounding up the total and rounding down the total number of children, the total number of non-homeless children is at maximum, 8 children. But clearly, the cohort must be 40-25 = 15. There can't be more graduates than there are total children which indicates that there may be some error in the data collection. For this reason, we've chosen to simply remove these rows and take the questionable credibility of our data set into consideration during our conclusion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1602, 7)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradRates_1819 = gradRates_1819[gradRates_1819['NOTHOM_rate'] < 100]\n",
    "gradRates_1819.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RLA and Math Scores 2018-2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Math scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the graduation rates data set, we elected to only include the columns regarding all the students and the homeless students. The code block below imports the data set, extracts our desired columns, removes the inserted privacy values and then uses our converter function to turn the strings into usable data (the same process as graduation rate data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "#Import the csv\n",
    "math_achieve = pd.read_csv('data/achievement/math_achieve_1819.csv', low_memory=False)\n",
    "\n",
    "math_achieve = math_achieve[['STNAM', 'ALL_MTH00numvalid_1819', 'ALL_MTH00pctprof_1819','HOM_MTH00numvalid_1819', 'HOM_MTH00pctprof_1819']]\n",
    "\n",
    "#Replace nonsense data with NaN and delete\n",
    "math_achieve.replace(nonsense, float(\"NaN\"), inplace=True)\n",
    "math_achieve = math_achieve.dropna() \n",
    "\n",
    "# Convert strings to usable data\n",
    "convertCols = ['ALL_MTH00pctprof_1819', 'HOM_MTH00numvalid_1819', 'HOM_MTH00pctprof_1819' ]\n",
    "for col in convertCols: \n",
    "    math_achieve[col] = math_achieve[col].apply(clean)\n",
    "math_achieve = math_achieve.dropna()\n",
    "\n",
    "#Convert strings to int64 \n",
    "for cols in convertCols: \n",
    "    math_achieve[col] = math_achieve[col].astype(np.int64)\n",
    "\n",
    "#Check if any empty, 'PS', or NaN values exist in the df\n",
    "print(('PS' in math_achieve) | ('' in math_achieve) | (math_achieve.isna().values.any()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code just gives a basic overview of our dataset so far:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STNAM                      object\n",
      "ALL_MTH00numvalid_1819      int64\n",
      "ALL_MTH00pctprof_1819     float64\n",
      "HOM_MTH00numvalid_1819      int64\n",
      "HOM_MTH00pctprof_1819       int64\n",
      "dtype: object\n",
      "(5499, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_MTH00numvalid_1819</th>\n",
       "      <th>ALL_MTH00pctprof_1819</th>\n",
       "      <th>HOM_MTH00numvalid_1819</th>\n",
       "      <th>HOM_MTH00pctprof_1819</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5499.000000</td>\n",
       "      <td>5499.000000</td>\n",
       "      <td>5499.000000</td>\n",
       "      <td>5499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>507.015276</td>\n",
       "      <td>41.044554</td>\n",
       "      <td>39.445899</td>\n",
       "      <td>32.810875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>349.072212</td>\n",
       "      <td>16.361348</td>\n",
       "      <td>31.511529</td>\n",
       "      <td>14.068347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>262.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>394.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>678.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5748.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ALL_MTH00numvalid_1819  ALL_MTH00pctprof_1819  HOM_MTH00numvalid_1819  \\\n",
       "count             5499.000000            5499.000000             5499.000000   \n",
       "mean               507.015276              41.044554               39.445899   \n",
       "std                349.072212              16.361348               31.511529   \n",
       "min                 34.000000               4.000000               16.000000   \n",
       "25%                262.000000              29.000000               20.000000   \n",
       "50%                394.000000              39.000000               30.000000   \n",
       "75%                678.000000              52.000000               46.000000   \n",
       "max               5748.000000              94.000000              409.000000   \n",
       "\n",
       "       HOM_MTH00pctprof_1819  \n",
       "count            5499.000000  \n",
       "mean               32.810875  \n",
       "std                14.068347  \n",
       "min                 7.000000  \n",
       "25%                24.000000  \n",
       "50%                30.000000  \n",
       "75%                44.000000  \n",
       "max                92.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(math_achieve.dtypes)\n",
    "print(math_achieve.shape)\n",
    "math_achieve.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just on a preliminary analysis, the percent proficiencies of all students is slightly higher than that of the homeless population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right now, the \"all\" student category includes homeless scores. For the sake of a better comparison, we're going to make two new columns: the number of non-homeless students that received a score (NOTHOM_numValid) and the percentage of non-homeless students that received a proficient score (NOTHOM_pctprof). We use the exact same logic as described in the graduation rate section. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAM</th>\n",
       "      <th>ALL_MTH00numvalid_1819</th>\n",
       "      <th>ALL_MTH00pctprof_1819</th>\n",
       "      <th>NOTHOM_numvalid</th>\n",
       "      <th>NOTHOM_pctprof</th>\n",
       "      <th>HOM_MTH00numvalid_1819</th>\n",
       "      <th>HOM_MTH00pctprof_1819</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>424</td>\n",
       "      <td>50.0</td>\n",
       "      <td>408</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>479</td>\n",
       "      <td>37.0</td>\n",
       "      <td>463</td>\n",
       "      <td>36</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>270</td>\n",
       "      <td>51.0</td>\n",
       "      <td>243</td>\n",
       "      <td>46</td>\n",
       "      <td>27</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>764</td>\n",
       "      <td>46.0</td>\n",
       "      <td>747</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>546</td>\n",
       "      <td>53.0</td>\n",
       "      <td>530</td>\n",
       "      <td>52</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STNAM  ALL_MTH00numvalid_1819  ALL_MTH00pctprof_1819  NOTHOM_numvalid  \\\n",
       "4    ALABAMA                     424                   50.0              408   \n",
       "7    ALABAMA                     479                   37.0              463   \n",
       "78   ALABAMA                     270                   51.0              243   \n",
       "128  ALABAMA                     764                   46.0              747   \n",
       "150  ALABAMA                     546                   53.0              530   \n",
       "\n",
       "     NOTHOM_pctprof  HOM_MTH00numvalid_1819  HOM_MTH00pctprof_1819  \n",
       "4                49                      16                     30  \n",
       "7                36                      16                     30  \n",
       "78               46                      27                     49  \n",
       "128              45                      17                     30  \n",
       "150              52                      16                     30  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Disable the double chain operation warning \n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n",
    "# Number of valid students \n",
    "math_achieve['NOTHOM_numvalid'] = math_achieve['ALL_MTH00numvalid_1819'] - math_achieve['HOM_MTH00numvalid_1819']\n",
    "\n",
    "# Calculate the approximate total number of proficient students \n",
    "math_achieve['NOTHOM_numProf'] = np.int64((math_achieve['ALL_MTH00numvalid_1819'] * (math_achieve['ALL_MTH00pctprof_1819'] / 100)) - (math_achieve['HOM_MTH00numvalid_1819'] * (math_achieve['HOM_MTH00pctprof_1819'] / 100)))\n",
    "\n",
    "# Use number of proficient students to find the non-homeless percetnage \n",
    "math_achieve['NOTHOM_pctprof'] = np.int64(np.round((math_achieve['NOTHOM_numProf'] / math_achieve['ALL_MTH00numvalid_1819']) * 100, 0))\n",
    "\n",
    "# Get rid of intermediary column \n",
    "math_achieve.pop('NOTHOM_numProf')\n",
    "\n",
    "# Rearrange the columns \n",
    "col = math_achieve.pop('NOTHOM_numvalid')\n",
    "math_achieve.insert(3, col.name, col)\n",
    "col = math_achieve.pop('NOTHOM_pctprof')\n",
    "math_achieve.insert(4, col.name, col)\n",
    "\n",
    "\n",
    "math_achieve.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, rename the columns to make it easier to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAM</th>\n",
       "      <th>ALL_numvalid</th>\n",
       "      <th>ALL_pctprof</th>\n",
       "      <th>NOTHOM_numvalid</th>\n",
       "      <th>NOTHOM_pctprof</th>\n",
       "      <th>HOM_numvalid</th>\n",
       "      <th>HOM_pctprof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>424</td>\n",
       "      <td>50.0</td>\n",
       "      <td>408</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>479</td>\n",
       "      <td>37.0</td>\n",
       "      <td>463</td>\n",
       "      <td>36</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>270</td>\n",
       "      <td>51.0</td>\n",
       "      <td>243</td>\n",
       "      <td>46</td>\n",
       "      <td>27</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>764</td>\n",
       "      <td>46.0</td>\n",
       "      <td>747</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>546</td>\n",
       "      <td>53.0</td>\n",
       "      <td>530</td>\n",
       "      <td>52</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STNAM  ALL_numvalid  ALL_pctprof  NOTHOM_numvalid  NOTHOM_pctprof  \\\n",
       "4    ALABAMA           424         50.0              408              49   \n",
       "7    ALABAMA           479         37.0              463              36   \n",
       "78   ALABAMA           270         51.0              243              46   \n",
       "128  ALABAMA           764         46.0              747              45   \n",
       "150  ALABAMA           546         53.0              530              52   \n",
       "\n",
       "     HOM_numvalid  HOM_pctprof  \n",
       "4              16           30  \n",
       "7              16           30  \n",
       "78             27           49  \n",
       "128            17           30  \n",
       "150            16           30  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math_achieve = math_achieve.rename(columns={\n",
    "    \"ALL_MTH00numvalid_1819\": \"ALL_numvalid\", \n",
    "    \"ALL_MTH00pctprof_1819\" : \"ALL_pctprof\", \n",
    "    \"HOM_MTH00numvalid_1819\": \"HOM_numvalid\", \n",
    "    \"HOM_MTH00pctprof_1819\" : \"HOM_pctprof\"})\n",
    "math_achieve.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_numvalid</th>\n",
       "      <th>ALL_pctprof</th>\n",
       "      <th>NOTHOM_numvalid</th>\n",
       "      <th>NOTHOM_pctprof</th>\n",
       "      <th>HOM_numvalid</th>\n",
       "      <th>HOM_pctprof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5499.000000</td>\n",
       "      <td>5499.000000</td>\n",
       "      <td>5499.000000</td>\n",
       "      <td>5499.000000</td>\n",
       "      <td>5499.000000</td>\n",
       "      <td>5499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>507.015276</td>\n",
       "      <td>41.044554</td>\n",
       "      <td>467.569376</td>\n",
       "      <td>37.585561</td>\n",
       "      <td>39.445899</td>\n",
       "      <td>32.810875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>349.072212</td>\n",
       "      <td>16.361348</td>\n",
       "      <td>344.179497</td>\n",
       "      <td>16.179774</td>\n",
       "      <td>31.511529</td>\n",
       "      <td>14.068347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>262.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>394.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>355.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>678.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>641.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5748.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>5567.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ALL_numvalid  ALL_pctprof  NOTHOM_numvalid  NOTHOM_pctprof  \\\n",
       "count   5499.000000  5499.000000      5499.000000     5499.000000   \n",
       "mean     507.015276    41.044554       467.569376       37.585561   \n",
       "std      349.072212    16.361348       344.179497       16.179774   \n",
       "min       34.000000     4.000000         0.000000        0.000000   \n",
       "25%      262.000000    29.000000       227.000000       25.000000   \n",
       "50%      394.000000    39.000000       355.000000       36.000000   \n",
       "75%      678.000000    52.000000       641.000000       48.000000   \n",
       "max     5748.000000    94.000000      5567.000000       91.000000   \n",
       "\n",
       "       HOM_numvalid  HOM_pctprof  \n",
       "count   5499.000000  5499.000000  \n",
       "mean      39.445899    32.810875  \n",
       "std       31.511529    14.068347  \n",
       "min       16.000000     7.000000  \n",
       "25%       20.000000    24.000000  \n",
       "50%       30.000000    30.000000  \n",
       "75%       46.000000    44.000000  \n",
       "max      409.000000    92.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math_achieve.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike the graduation rates section, our data in the non-homeless category stays within the bounds of normal percentages. But, the minimum of 0 students does seem a little concerning. The next block of code prints our the rows with this value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAM</th>\n",
       "      <th>ALL_numvalid</th>\n",
       "      <th>ALL_pctprof</th>\n",
       "      <th>NOTHOM_numvalid</th>\n",
       "      <th>NOTHOM_pctprof</th>\n",
       "      <th>HOM_numvalid</th>\n",
       "      <th>HOM_pctprof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6436</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>129</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6607</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>136</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6663</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>39</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9125</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>34</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14816</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>132</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83113</th>\n",
       "      <td>UTAH</td>\n",
       "      <td>139</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            STNAM  ALL_numvalid  ALL_pctprof  NOTHOM_numvalid  NOTHOM_pctprof  \\\n",
       "6436   CALIFORNIA           129         42.0                0               0   \n",
       "6607   CALIFORNIA           136         62.0                0               0   \n",
       "6663   CALIFORNIA            39         24.0                2               0   \n",
       "9125   CALIFORNIA            34         15.0                3               0   \n",
       "14816  CALIFORNIA           132          7.0                0               0   \n",
       "83113        UTAH           139         12.0                8               0   \n",
       "\n",
       "       HOM_numvalid  HOM_pctprof  \n",
       "6436            129           42  \n",
       "6607            136           62  \n",
       "6663             37           24  \n",
       "9125             31           15  \n",
       "14816           132            7  \n",
       "83113           131           12  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math_achieve[math_achieve['NOTHOM_pctprof'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering that the original data set had many more demographic categories besides homelessness, it probably isn't the case that these 6 schools had an entire student body consisting of just homeless students. These discrepencies could've been due to finding the average of ranged values in our cleaning, but we thought it was fair to once again chalk these up to some error in the data set. There were, after all, nearly 90,000 observations initially, so we removed them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_numvalid</th>\n",
       "      <th>ALL_pctprof</th>\n",
       "      <th>NOTHOM_numvalid</th>\n",
       "      <th>NOTHOM_pctprof</th>\n",
       "      <th>HOM_numvalid</th>\n",
       "      <th>HOM_pctprof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5493.000000</td>\n",
       "      <td>5493.000000</td>\n",
       "      <td>5493.000000</td>\n",
       "      <td>5493.000000</td>\n",
       "      <td>5493.000000</td>\n",
       "      <td>5493.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>507.458220</td>\n",
       "      <td>41.059894</td>\n",
       "      <td>468.077735</td>\n",
       "      <td>37.626616</td>\n",
       "      <td>39.380484</td>\n",
       "      <td>32.817222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>349.001951</td>\n",
       "      <td>16.351292</td>\n",
       "      <td>344.023314</td>\n",
       "      <td>16.140820</td>\n",
       "      <td>31.429272</td>\n",
       "      <td>14.060299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>262.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>395.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>678.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>641.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5748.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>5567.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ALL_numvalid  ALL_pctprof  NOTHOM_numvalid  NOTHOM_pctprof  \\\n",
       "count   5493.000000  5493.000000      5493.000000     5493.000000   \n",
       "mean     507.458220    41.059894       468.077735       37.626616   \n",
       "std      349.001951    16.351292       344.023314       16.140820   \n",
       "min       41.000000     4.000000         2.000000        1.000000   \n",
       "25%      262.000000    29.000000       228.000000       25.000000   \n",
       "50%      395.000000    39.000000       356.000000       36.000000   \n",
       "75%      678.000000    52.000000       641.000000       48.000000   \n",
       "max     5748.000000    94.000000      5567.000000       91.000000   \n",
       "\n",
       "       HOM_numvalid  HOM_pctprof  \n",
       "count   5493.000000  5493.000000  \n",
       "mean      39.380484    32.817222  \n",
       "std       31.429272    14.060299  \n",
       "min       16.000000     7.000000  \n",
       "25%       20.000000    24.000000  \n",
       "50%       30.000000    30.000000  \n",
       "75%       46.000000    44.000000  \n",
       "max      409.000000    92.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math_achieve = math_achieve[math_achieve['NOTHOM_pctprof'] > 0]\n",
    "math_achieve.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RLA Scores "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the graduation rates data set, we elected to only include the columns regarding all the students and the homeless students. The code block below imports the data set, extracts our desired columns, removes the inserted privacy values and then uses our converter function to turn the strings into usable data (the exact same process as graduation rates and math achievement cleaning)  ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "lea_achieve = pd.read_csv('data/achievement/rla_achieve_1819.csv', low_memory=False)\n",
    "\n",
    "lea_achieve = lea_achieve[['STNAM', 'ALL_RLA00numvalid_1819', 'ALL_RLA00pctprof_1819','HOM_RLA00numvalid_1819', 'HOM_RLA00pctprof_1819']]\n",
    "\n",
    "#Replace nonsense data with NaN and get rid of it\n",
    "lea_achieve.replace(nonsense, float(\"NaN\"), inplace=True)\n",
    "lea_achieve = lea_achieve.dropna() \n",
    "\n",
    "# Convert strings to usable data \n",
    "lea_achieve_sub = ['ALL_RLA00pctprof_1819','HOM_RLA00numvalid_1819', 'HOM_RLA00pctprof_1819']\n",
    "for cols in lea_achieve_sub: \n",
    "    lea_achieve[cols] = lea_achieve[cols].apply(clean)\n",
    "lea_achieve = lea_achieve.dropna()\n",
    "\n",
    "# Convert the columns into int64's \n",
    "for cols in lea_achieve_sub: \n",
    "    lea_achieve[cols] = lea_achieve[cols].astype(np.int64)\n",
    "\n",
    "#Check if any empty, 'PS', or NaN values exist in the df\n",
    "print(('PS' in lea_achieve) | ('' in lea_achieve) | (lea_achieve.isna().values.any()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print out the stats of our data set again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STNAM                     object\n",
      "ALL_RLA00numvalid_1819     int64\n",
      "ALL_RLA00pctprof_1819      int64\n",
      "HOM_RLA00numvalid_1819     int64\n",
      "HOM_RLA00pctprof_1819      int64\n",
      "dtype: object\n",
      "(6579, 5)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_RLA00numvalid_1819</th>\n",
       "      <th>ALL_RLA00pctprof_1819</th>\n",
       "      <th>HOM_RLA00numvalid_1819</th>\n",
       "      <th>HOM_RLA00pctprof_1819</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6579.000000</td>\n",
       "      <td>6579.000000</td>\n",
       "      <td>6579.000000</td>\n",
       "      <td>6579.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>526.867609</td>\n",
       "      <td>43.373765</td>\n",
       "      <td>37.200638</td>\n",
       "      <td>34.753154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>366.666595</td>\n",
       "      <td>14.813905</td>\n",
       "      <td>29.060586</td>\n",
       "      <td>12.938914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>269.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>411.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>722.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7366.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ALL_RLA00numvalid_1819  ALL_RLA00pctprof_1819  HOM_RLA00numvalid_1819  \\\n",
       "count             6579.000000            6579.000000             6579.000000   \n",
       "mean               526.867609              43.373765               37.200638   \n",
       "std                366.666595              14.813905               29.060586   \n",
       "min                 34.000000               5.000000               16.000000   \n",
       "25%                269.000000              32.000000               20.000000   \n",
       "50%                411.000000              42.000000               28.000000   \n",
       "75%                722.000000              53.000000               43.000000   \n",
       "max               7366.000000              94.000000              399.000000   \n",
       "\n",
       "       HOM_RLA00pctprof_1819  \n",
       "count            6579.000000  \n",
       "mean               34.753154  \n",
       "std                12.938914  \n",
       "min                 7.000000  \n",
       "25%                30.000000  \n",
       "50%                30.000000  \n",
       "75%                44.000000  \n",
       "max                92.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(lea_achieve.dtypes)\n",
    "print(lea_achieve.shape)\n",
    "lea_achieve.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to math, there is just a slight difference in the percent proficiencies of all the students and the homeless students in the state tests for RLA. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, similarly to math, we're going to create two new columns: the number of non-homeless students who got a score and the percentage of those students that got a proficient score. We again use the same logic employed during the cleaning of the graduation rates.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAM</th>\n",
       "      <th>ALL_RLA00numvalid_1819</th>\n",
       "      <th>ALL_RLA00pctprof_1819</th>\n",
       "      <th>NOTHOM_numvalid</th>\n",
       "      <th>NOTHOM_pctprof</th>\n",
       "      <th>HOM_RLA00numvalid_1819</th>\n",
       "      <th>HOM_RLA00pctprof_1819</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>424</td>\n",
       "      <td>47</td>\n",
       "      <td>408</td>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>270</td>\n",
       "      <td>51</td>\n",
       "      <td>243</td>\n",
       "      <td>48</td>\n",
       "      <td>27</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>284</td>\n",
       "      <td>47</td>\n",
       "      <td>266</td>\n",
       "      <td>44</td>\n",
       "      <td>18</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>316</td>\n",
       "      <td>46</td>\n",
       "      <td>298</td>\n",
       "      <td>44</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>235</td>\n",
       "      <td>40</td>\n",
       "      <td>216</td>\n",
       "      <td>36</td>\n",
       "      <td>19</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STNAM  ALL_RLA00numvalid_1819  ALL_RLA00pctprof_1819  NOTHOM_numvalid  \\\n",
       "4    ALABAMA                     424                     47              408   \n",
       "78   ALABAMA                     270                     51              243   \n",
       "233  ALABAMA                     284                     47              266   \n",
       "274  ALABAMA                     316                     46              298   \n",
       "275  ALABAMA                     235                     40              216   \n",
       "\n",
       "     NOTHOM_pctprof  HOM_RLA00numvalid_1819  HOM_RLA00pctprof_1819  \n",
       "4                45                      16                     49  \n",
       "78               48                      27                     30  \n",
       "233              44                      18                     49  \n",
       "274              44                      18                     30  \n",
       "275              36                      19                     49  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of valid students \n",
    "lea_achieve['NOTHOM_numvalid'] = lea_achieve['ALL_RLA00numvalid_1819'] - lea_achieve['HOM_RLA00numvalid_1819']\n",
    "\n",
    "# Find the approximate total number of proficient students \n",
    "lea_achieve['NOTHOM_numProf'] = np.int64((lea_achieve['ALL_RLA00numvalid_1819'] * (lea_achieve['ALL_RLA00pctprof_1819'] / 100)) - (lea_achieve['HOM_RLA00numvalid_1819'] * (lea_achieve['HOM_RLA00pctprof_1819'] / 100)))\n",
    "\n",
    "# Use the approximate number of proficient students to find the percentage of proficient non homeless students \n",
    "lea_achieve['NOTHOM_pctprof'] = np.int64(np.round((lea_achieve['NOTHOM_numProf'] / lea_achieve['ALL_RLA00numvalid_1819']) * 100, 0)) \n",
    "\n",
    "# Delete intermediary column\n",
    "lea_achieve.pop('NOTHOM_numProf')\n",
    "\n",
    "# Rearrange the columns \n",
    "col = lea_achieve.pop('NOTHOM_numvalid') \n",
    "lea_achieve.insert(3, col.name, col)\n",
    "col = lea_achieve.pop('NOTHOM_pctprof')\n",
    "lea_achieve.insert(4, col.name, col)\n",
    "\n",
    "\n",
    "lea_achieve.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also edit the column names so they're easier to work with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_numvalid</th>\n",
       "      <th>ALL_pctprof</th>\n",
       "      <th>NOTHOM_numvalid</th>\n",
       "      <th>NOTHOM_pctprof</th>\n",
       "      <th>HOM_numvalid</th>\n",
       "      <th>HOM_pctprof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6579.000000</td>\n",
       "      <td>6579.000000</td>\n",
       "      <td>6579.000000</td>\n",
       "      <td>6579.000000</td>\n",
       "      <td>6579.000000</td>\n",
       "      <td>6579.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>526.867609</td>\n",
       "      <td>43.373765</td>\n",
       "      <td>489.666971</td>\n",
       "      <td>39.884025</td>\n",
       "      <td>37.200638</td>\n",
       "      <td>34.753154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>366.666595</td>\n",
       "      <td>14.813905</td>\n",
       "      <td>363.105808</td>\n",
       "      <td>14.759718</td>\n",
       "      <td>29.060586</td>\n",
       "      <td>12.938914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>269.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>411.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>722.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7366.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>7347.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ALL_numvalid  ALL_pctprof  NOTHOM_numvalid  NOTHOM_pctprof  \\\n",
       "count   6579.000000  6579.000000      6579.000000     6579.000000   \n",
       "mean     526.867609    43.373765       489.666971       39.884025   \n",
       "std      366.666595    14.813905       363.105808       14.759718   \n",
       "min       34.000000     5.000000         0.000000        0.000000   \n",
       "25%      269.000000    32.000000       234.000000       29.000000   \n",
       "50%      411.000000    42.000000       372.000000       39.000000   \n",
       "75%      722.000000    53.000000       683.000000       50.000000   \n",
       "max     7366.000000    94.000000      7347.000000       91.000000   \n",
       "\n",
       "       HOM_numvalid  HOM_pctprof  \n",
       "count   6579.000000  6579.000000  \n",
       "mean      37.200638    34.753154  \n",
       "std       29.060586    12.938914  \n",
       "min       16.000000     7.000000  \n",
       "25%       20.000000    30.000000  \n",
       "50%       28.000000    30.000000  \n",
       "75%       43.000000    44.000000  \n",
       "max      399.000000    92.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lea_achieve = lea_achieve.rename(columns={\n",
    "    \"ALL_RLA00numvalid_1819\": \"ALL_numvalid\", \n",
    "    \"ALL_RLA00pctprof_1819\" : \"ALL_pctprof\", \n",
    "    \"HOM_RLA00numvalid_1819\": \"HOM_numvalid\", \n",
    "    \"HOM_RLA00pctprof_1819\" : \"HOM_pctprof\"})\n",
    "\n",
    "lea_achieve.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the non-homeless data does not exceed the limits of percentages but just like in math, there is a 0% minimum which is slightly concerning because of it's very low likelihood. Once again, we're going to investigate these schools a little closer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STNAM</th>\n",
       "      <th>ALL_numvalid</th>\n",
       "      <th>ALL_pctprof</th>\n",
       "      <th>NOTHOM_numvalid</th>\n",
       "      <th>NOTHOM_pctprof</th>\n",
       "      <th>HOM_numvalid</th>\n",
       "      <th>HOM_pctprof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>52</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6439</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>129</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6610</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>136</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14820</th>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>131</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46993</th>\n",
       "      <td>MISSOURI</td>\n",
       "      <td>55</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83088</th>\n",
       "      <td>UTAH</td>\n",
       "      <td>139</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            STNAM  ALL_numvalid  ALL_pctprof  NOTHOM_numvalid  NOTHOM_pctprof  \\\n",
       "5698   CALIFORNIA            52           15               17               0   \n",
       "6439   CALIFORNIA           129           42                0               0   \n",
       "6610   CALIFORNIA           136           67                0               0   \n",
       "9128   CALIFORNIA            34           24                3               0   \n",
       "14820  CALIFORNIA           131           17                0               0   \n",
       "46993    MISSOURI            55           15               28               0   \n",
       "83088        UTAH           139           12                8               0   \n",
       "\n",
       "       HOM_numvalid  HOM_pctprof  \n",
       "5698             35           24  \n",
       "6439            129           42  \n",
       "6610            136           67  \n",
       "9128             31           24  \n",
       "14820           131           17  \n",
       "46993            27           30  \n",
       "83088           131           12  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lea_achieve[lea_achieve['NOTHOM_pctprof'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, some of these values are probably off because of how we handled the ranged values from the original data set. But there is a high likelihood that some of these rows have some misreported or mistakenly reported data considering that some supposedly have an entire student body of homeless students and the original data set had ~90,000 rows. For this reason, we've once again chosen to simply remove these from the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_numvalid</th>\n",
       "      <th>ALL_pctprof</th>\n",
       "      <th>NOTHOM_numvalid</th>\n",
       "      <th>NOTHOM_pctprof</th>\n",
       "      <th>HOM_numvalid</th>\n",
       "      <th>HOM_pctprof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>527.325928</td>\n",
       "      <td>43.390749</td>\n",
       "      <td>490.180006</td>\n",
       "      <td>39.926506</td>\n",
       "      <td>37.145922</td>\n",
       "      <td>34.757304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>366.589891</td>\n",
       "      <td>14.800076</td>\n",
       "      <td>362.958348</td>\n",
       "      <td>14.710029</td>\n",
       "      <td>28.981822</td>\n",
       "      <td>12.932989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>269.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>411.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>722.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7366.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>7347.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ALL_numvalid  ALL_pctprof  NOTHOM_numvalid  NOTHOM_pctprof  \\\n",
       "count   6572.000000  6572.000000      6572.000000     6572.000000   \n",
       "mean     527.325928    43.390749       490.180006       39.926506   \n",
       "std      366.589891    14.800076       362.958348       14.710029   \n",
       "min       36.000000     5.000000         2.000000        1.000000   \n",
       "25%      269.000000    32.000000       234.000000       29.000000   \n",
       "50%      411.000000    42.000000       372.000000       39.000000   \n",
       "75%      722.000000    53.000000       683.000000       50.000000   \n",
       "max     7366.000000    94.000000      7347.000000       91.000000   \n",
       "\n",
       "       HOM_numvalid  HOM_pctprof  \n",
       "count   6572.000000  6572.000000  \n",
       "mean      37.145922    34.757304  \n",
       "std       28.981822    12.932989  \n",
       "min       16.000000     7.000000  \n",
       "25%       20.000000    30.000000  \n",
       "50%       28.000000    30.000000  \n",
       "75%       43.000000    44.000000  \n",
       "max      399.000000    92.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lea_achieve = lea_achieve[lea_achieve['NOTHOM_pctprof'] > 0]\n",
    "lea_achieve.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregating the data by state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we’ve looked at the distribution of our data by school, we can begin to aggregate it by state. For graduation rates, we found the average graduation rate of all, homeless, and non-homeless students by state. We did the same for the math and rla achievements' percent proficiencies, but elected to leave out the numvalid category of both the math and rla achievement data sets because they no longer provided useful information for our analysis. We also added in the overall mean of the grad rates and percent proficiences of all, homeless, and nonhomeless students as a row named \"Total\" in each dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find mean grad rate of students by state \n",
    "mean_gradRates = gradRates_1819.groupby('STNAM')[['ALL_rate', 'NOTHOM_rate', 'HOM_rate']].mean()\n",
    "\n",
    "# Find mean percent proficiency of students by state. \n",
    "mean_state_m = math_achieve.groupby('STNAM')[['ALL_pctprof', 'NOTHOM_pctprof', 'HOM_pctprof']].mean()\n",
    "mean_state_l = lea_achieve.groupby('STNAM')[['ALL_pctprof', 'NOTHOM_pctprof', 'HOM_pctprof']].mean()\n",
    "\n",
    "\n",
    "# Add a totalmean row to get a mean pct prof for each category of student\n",
    "mean_state_m.loc['Total'] = mean_state_m.mean()\n",
    "mean_state_l.loc['Total'] = mean_state_l.mean()\n",
    "\n",
    "#Add grad rate overall mean to get an overall mean grad rate for each category of student \n",
    "mean_gradRates.loc['Total'] = mean_gradRates.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Choropleth GeoDataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The geodata json file comes with a bunch of stuff we don't need. We simply need the state name and the geometries of the states. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_gdf = gpd.read_file('data\\Shapefiles\\mygeodata_merged.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that a lot of the manipulations we need to do aren't supported by GeoDataFrames. That is why we must turn the GeoDataFrame into a Pandas Dataframe.\n",
    "\n",
    "We simply capitalize all of the state names in the geodataframe, then set the index of that dataframe to be the state names (just like the format we have for our graduation and achievement data). Then we strip the geodataframe of all columns except the geometry column. Afterwards, we drop some territories and resize some states for visual purposes.\n",
    "\n",
    "Finally, we perform an inner merge with the mean graduation data frame, average percent proficiency in math data frame, and the average percent proficiency in RLA data frame to create the choropleth dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_df = pd.DataFrame(US_gdf)\n",
    "\n",
    "# Capitalize all the State names \n",
    "US_df['NAME'] = US_df['NAME'].str.upper()\n",
    "\n",
    "# Set indices to be the state names\n",
    "US_df.set_index('NAME', inplace=True)\n",
    "\n",
    "# Strip the dataframe of all columns except geometry \n",
    "US_df = US_df[['geometry']]\n",
    "\n",
    "# Turn Hawaii and Alaska into a geoseries, resize it, and then reinsert it into the dataframe\n",
    "hi= GeoSeries(US_df.at['HAWAII', 'geometry'])\n",
    "hi = hi.translate(80,40)\n",
    "hi = hi.scale(xfact=3.0, yfact=3.0, zfact=1.0, origin='center')\n",
    "US_df.at['HAWAII','geometry'] = hi[0]\n",
    "\n",
    "al = GeoSeries(US_df.at['ALASKA', 'geometry'])\n",
    "al = al.scale(xfact=0.7, yfact=0.7, zfact=1.0, origin='center')\n",
    "US_df.at['ALASKA', 'geometry'] = al[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_df = US_df.drop(['COMMONWEALTH OF THE NORTHERN MARIANA ISLANDS', 'UNITED STATES VIRGIN ISLANDS','GUAM', 'AMERICAN SAMOA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create the merged dataframes to be used for choropleths \n",
    "grad_choro = pd.merge(US_df, mean_gradRates, how='left', left_index=True, right_index=True)\n",
    "grad_choro['diff'] =  grad_choro['NOTHOM_rate']- grad_choro['HOM_rate']\n",
    "grad_gdf = gpd.GeoDataFrame(grad_choro)\n",
    "\n",
    "math_choro = pd.merge(US_df, mean_state_m, how='left', left_index=True, right_index=True)\n",
    "math_choro['diff'] = math_choro['NOTHOM_pctprof'] - math_choro['HOM_pctprof']\n",
    "math_gdf = gpd.GeoDataFrame(math_choro)\n",
    "\n",
    "rla_choro = pd.merge(US_df, mean_state_l, how='left', left_index=True, right_index=True)\n",
    "rla_choro['diff'] = rla_choro['NOTHOM_pctprof'] - rla_choro['HOM_pctprof']\n",
    "rla_gdf = gpd.GeoDataFrame(rla_choro)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for the graduation data set, the merge cuts out the \"Bureau of Indian Education\" from the mean_gradRates data set. This is okay considering that there were virtually no US map shapefiles containing all of the Native American boundaries. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally speaking, our data can be divided into two categories: achievement results and graduation rates. We will take a similar approach to analyze both since they contain similar sub-categories after cleaning that have direct parallels with each other. However, there are slight differences that will need individual attention, so we will start with graduation rates and then do the same to achievement results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by first showing how many of the original observations are from each state. This is important in terms of determining the validity of the observations from each state. Recall that each of our data sets contained privacy measures that obfuscated the data from populations with smaller student bodies. If only a handful of observations are coming out of one state, then that makes it much more likely that, that state's data mostly consists of estimated data and could be high balling or low balling our overall numbers. Furthermore, considering that each of these data sets contains thousands of observations whose correctness is up to the school district sending each observation, it's most likely the case that some of these observations are untrue and misreported. In states with just a handful of observations, these untrue observations could heavily skew their averages, which is something we address in our outliers section. And finally, knowing which states contributed more data to our analysis is important in determining the boundaries for our conclusions. \n",
    "\n",
    "Note that this requires using the data sets we had before aggregating it into state by state data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which states have the most influence? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### State Influence for Graduation Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Countplot of States for Graduation Data')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize': (10,10)})\n",
    "sns.countplot(y='STNAM', order=gradRates_1819['STNAM'].value_counts().index, data=gradRates_1819)\n",
    "plt.title(\"Countplot of States for Graduation Data\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the graph above, there seem to be only a couple of states with very high graduation rates—California is far in the lead, followed by Texas, after which there is a large drop off."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### State Influence for Math Achievement Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Countplot of States for Math Achievement Data')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='STNAM', order=math_achieve['STNAM'].value_counts().index, data=math_achieve)\n",
    "plt.title(\"Countplot of States for Math Achievement Data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the count plot above, we see that California is by far in the lead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Countplot of States for RLA Achievement Data')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='STNAM', order=lea_achieve['STNAM'].value_counts().index, data=lea_achieve)\n",
    "plt.title(\"Countplot of States for RLA Achievement Data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at these graphs, it is clear that California has, by far, the highest number of valid test results. For the achievement data, Florida, Ohio, and Georgia follow, with similar magnitudes of valid results. As a whole, most results in the middle are about the same, with a couple of close to 0 results at the other extreme (note: none of them are 0, the plot just makes them look like that because of the huge range of counts)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the distribution of our data by state and handling outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first take a look at the general description of our aggregated data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grad rates: \n",
      "         ALL_rate  NOTHOM_rate   HOM_rate\n",
      "count  47.000000    47.000000  47.000000\n",
      "mean   76.647904    79.061475  59.667455\n",
      "std     9.562394     9.745498   9.731074\n",
      "min    32.000000    32.000000  30.000000\n",
      "25%    71.535714    74.130435  54.362672\n",
      "50%    78.714286    80.978723  60.818182\n",
      "75%    81.693182    84.745750  65.234043\n",
      "max    92.500000    93.000000  76.500000\n"
     ]
    }
   ],
   "source": [
    "print('Grad rates: \\n', mean_gradRates.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean pctProf Math:\n",
      "        ALL_pctprof  NOTHOM_pctprof  HOM_pctprof\n",
      "count    50.000000       50.000000    50.000000\n",
      "mean     38.186823       35.264843    30.569988\n",
      "std       9.813257        9.743385     6.590750\n",
      "min      18.000000       16.000000    15.000000\n",
      "25%      33.019231       30.125000    25.817949\n",
      "50%      37.859649       35.103333    29.236111\n",
      "75%      43.269322       40.065611    35.065296\n",
      "max      70.673333       67.973333    50.926667 \n",
      "\n",
      " Mean pctProf RLA: \n",
      "        ALL_pctprof  NOTHOM_pctprof  HOM_pctprof\n",
      "count    50.000000       50.000000    50.000000\n",
      "mean     41.393629       38.556395    32.611427\n",
      "std       8.339906        8.491700     5.336696\n",
      "min      28.659091       19.000000    23.000000\n",
      "25%      34.711948       31.708763    29.616667\n",
      "50%      41.657132       38.729017    31.536765\n",
      "75%      45.149399       41.741203    35.120833\n",
      "max      65.000000       62.339286    49.594340\n"
     ]
    }
   ],
   "source": [
    "print('Mean pctProf Math:\\n', mean_state_m.describe(), '\\n\\n', 'Mean pctProf RLA: \\n', mean_state_l.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's nothing particularly concerning about our data sets on first glance in terms of outliers. But to be sure there aren't any concerning outliers, we're going to utilize the 1.5IQR rule for outliers. \n",
    "\n",
    "In the below block of code, we calculate and store the upper and lower \"whiskers\" for each of the columns of all three datasets. If any data point in their respective set is outside of these whiskers, then we'll consider them to be an outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad_IQR = 1.5*(mean_gradRates.describe().loc['75%'] - mean_gradRates.describe().loc['25%'])\n",
    "grad_upper_whiskers = mean_gradRates.describe().loc['75%'] + grad_IQR\n",
    "grad_lower_whiskers = mean_gradRates.describe().loc['25%'] - grad_IQR\n",
    "\n",
    "math_IQR = 1.5*(mean_state_m.describe().loc['75%'] - mean_state_m.describe().loc['25%'])\n",
    "math_upper_whiskers = mean_state_m.describe().loc['75%'] + math_IQR\n",
    "math_lower_whiskers = mean_state_m.describe().loc['25%'] - math_IQR\n",
    "\n",
    "lea_IQR = 1.5*(mean_state_l.describe().loc['75%'] - mean_state_l.describe().loc['25%'])\n",
    "lea_upper_whiskers = mean_state_l.describe().loc['75%'] + lea_IQR\n",
    "lea_lower_whiskers = mean_state_l.describe().loc['25%'] - lea_IQR\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next sections goes through the process of finding and handling outliers for each data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boxplot Graduation Rates by State and Outliers "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This piece of code create a boxplot showing the box and \"whiskers\" of the graduation rates of all, homeless, and non-homeless students. We're looking for any points of data lying outside of the whiskers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Category of Student vs Grad. Rates Boxplot')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the boxplot\n",
    "sns.set(rc={'figure.figsize': (7,7)})\n",
    "sns.boxplot( data=mean_gradRates)\n",
    "plt.ylabel(\"Percent proficiency\")\n",
    "plt.xlabel(\"Student Category\")\n",
    "plt.title(\"Category of Student vs Grad. Rates Boxplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen on the graph, there are 3 outliers, one in each category of student. \n",
    "\n",
    "We investigate these outliers more closely in the next block of code. It simply goes through the plotted dataframe that we made above and in this case, finds all values in the all, homeless, and non-homeless student categories that were less than their respective lower whisker. It then prints them out "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All:\n",
      "            ALL_rate  NOTHOM_rate  HOM_rate\n",
      "STNAM                                     \n",
      "LOUISIANA      32.0         32.0      30.0 \n",
      "\n",
      " Homeless: \n",
      "            ALL_rate  NOTHOM_rate  HOM_rate\n",
      "STNAM                                     \n",
      "KENTUCKY       88.0         89.0      30.0\n",
      "LOUISIANA      32.0         32.0      30.0 \n",
      "\n",
      " Non-Homeless: \n",
      "            ALL_rate  NOTHOM_rate  HOM_rate\n",
      "STNAM                                     \n",
      "LOUISIANA      32.0         32.0      30.0\n"
     ]
    }
   ],
   "source": [
    "#Find the lower outliers \n",
    "categories = ['ALL_rate', 'HOM_rate', 'NOTHOM_rate']\n",
    "for cat in categories: \n",
    "    locals()['grad_lower_outliers_' + cat] = mean_gradRates[mean_gradRates[cat] < grad_lower_whiskers[cat]]\n",
    "\n",
    "#Let's take a look at all these outliers \n",
    "print('All:\\n', grad_lower_outliers_ALL_rate, '\\n\\n', 'Homeless: \\n', grad_lower_outliers_HOM_rate, '\\n\\n', 'Non-Homeless: \\n', grad_lower_outliers_NOTHOM_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we refer to our graphs visualizing how much of our data is coming from each state. We see that both Kentucky and Louisiana are at the bottom of the list, in other words, the least amount of data points came out of these states. This is grounds to believe that their data is possibly improperly representing the true graduation rates of their student bodies. Because the original data set contained nearly 20,000 schools and their correctness was dependent on the schools sending the data, it's highly likely that the original data set contained some bad apples. We even encountered some of them while cleaning out data set and creating the (NOT_HOM) columns. Furthermore, recall that all our data sets utilized privacy measures which obfuscated the true numbers of students in areas with a low student body. We cut out most of them in our data cleaning, but included the averages of ranged estimates. These could possibly be misrepresenting the student populations and lowballing what they truly are. For these reasons, we believed it was perfectly reasonable to cut these states out from our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_gradRates.drop(index=['LOUISIANA','KENTUCKY'], inplace=True)\n",
    "\n",
    "# Recalculate the total row \n",
    "mean_gradRates.loc['Total'] = mean_gradRates.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_rate</th>\n",
       "      <th>NOTHOM_rate</th>\n",
       "      <th>HOM_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>77.404254</td>\n",
       "      <td>79.904762</td>\n",
       "      <td>61.015310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.840239</td>\n",
       "      <td>6.782264</td>\n",
       "      <td>7.561243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>60.833333</td>\n",
       "      <td>64.666667</td>\n",
       "      <td>46.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>55.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.714286</td>\n",
       "      <td>80.978723</td>\n",
       "      <td>61.198113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>81.636364</td>\n",
       "      <td>84.727273</td>\n",
       "      <td>65.468085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>92.500000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>76.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ALL_rate  NOTHOM_rate   HOM_rate\n",
       "count  45.000000    45.000000  45.000000\n",
       "mean   77.404254    79.904762  61.015310\n",
       "std     6.840239     6.782264   7.561243\n",
       "min    60.833333    64.666667  46.285714\n",
       "25%    72.000000    74.500000  55.333333\n",
       "50%    78.714286    80.978723  61.198113\n",
       "75%    81.636364    84.727273  65.468085\n",
       "max    92.500000    93.000000  76.500000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_gradRates.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this description, we can see that the standard deviation of all three categories has been considerably decreased (about 3 units), the minimum values have been replaced, and the mean of the Homeless student graduation rate has gone up by about 2 percent. The other two's means however have been relatively unchanged."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boxplot of Percent Proficiency of Math by State and Outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Category of Student vs % Prof Math Boxplot')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot \n",
    "sns.boxplot( data=mean_state_m)\n",
    "plt.ylabel(\"Percent proficiency\")\n",
    "plt.xlabel(\"Category of student\")\n",
    "plt.title(\"Category of Student vs % Prof Math Boxplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As visualized by the box plot, there is one outlier per category of student. Similarly to the graduation rates analysis above, the next blocks of code investigate the outliers closer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          ALL_pctprof  NOTHOM_pctprof  HOM_pctprof\n",
      "STNAM                                             \n",
      "VIRGINIA    70.673333       67.973333    50.926667 \n",
      "\n",
      "           ALL_pctprof  NOTHOM_pctprof  HOM_pctprof\n",
      "STNAM                                             \n",
      "VIRGINIA    70.673333       67.973333    50.926667 \n",
      "\n",
      "           ALL_pctprof  NOTHOM_pctprof  HOM_pctprof\n",
      "STNAM                                             \n",
      "VIRGINIA    70.673333       67.973333    50.926667\n"
     ]
    }
   ],
   "source": [
    "#Find the upper outliers \n",
    "categories = ['ALL_pctprof', 'NOTHOM_pctprof', 'HOM_pctprof']\n",
    "for cat in categories: \n",
    "    locals()['m_upper_outliers_' + cat] = mean_state_m[mean_state_m[cat] > math_upper_whiskers[cat]]\n",
    "#Let's take a look at all these outliers \n",
    "print(m_upper_outliers_ALL_pctprof, '\\n\\n', m_upper_outliers_NOTHOM_pctprof, '\\n\\n', m_upper_outliers_HOM_pctprof)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, Virginia was the outlier for all three categories and it wasn't particularly low in the math \"which state has the most influence\" graph. This implies that there could be some interesting phenomenon going on here and probably shouldn't be cut out from the data; this data set has no outliers to cut and will be used as is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boxplot of Percent Proficiency of RLA by State and Outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Category of Student vs % Prof RLA Boxplot')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8gcDAQOzZs0c6JEhUXjx9RS2VT+zBkcHVqVMHvr6+MDMzgxACQUFBLxxu06ZNw++//47g4GCGGxG9FPbgiIhIlniRCRERyRIDjoiIZIkBR0REssSAIyIiWapwV1FmZDyETsfrYoiICFAqFahR47Vin6twAafTCQYcERHpxUOUREQkSww4IiKSJQYcERHJEgOOiIhkiQFHRESyxIAjIiJZYsAREZEsMeCIiEiWGHBERCRLDDgiIpIlBlwFotFkYMGCucjM1Ji6FCKico8BV4FEROzEpUsXEB4eZupSiIjKPQZcBaHRZCAu7hCEEIiLi2UvjohIDwZcBRERsVMaRUGn07EXR0SkBwOugkhIiIdWmwcA0GrzkJAQb+KKiIjKNwZcBeHo6ASVKn/4PpXKDI6OTiauiIiofGPAVRBq9QAolQoAgFKpRL9+A01cERFR+caAqyBsbGrA2dkVCoUCzs4usLa2MXVJRETlmpmpC6Dnp1YPwK1bN9l7IyJ6DgohhDB1ES8iPT1LupqQiIgqN6VSgZo1rYp/roxrISIiKhMMOCIikiUGHBERyRIDjoiIZIkBR0REssSAIyIiWWLAERGRLDHgiIhIlhhwREQkSww4IiKSJQYcUQWj0WRgwYK5HNWdSA8GHFEFExGxE5cuXeCo7kR6MOCIKhCNJgNxcYcghEBcXCx7cUSlYMARVSARETul0TR0Oh17cUSlYMARVSAJCfHQavMAAFptHhIS4k1cEVH5xYAjqkAcHZ2gUuWPU6xSmcHR0cnEFRGVXww4ogpErR4ApVIBAFAqlRzdnagUDDiiCsTGpgacnV2hUCjg7OwCa2sbU5dEVG6ZmboAInoxavUA3Lp1k703Ij0UQghh6iJeRHp6lnQVGRERVW5KpQI1a1oV/1wZ10JERFQmGHBERCRLDDgiIpIlBhwREckSA46IiGSJAUdERLLEgCMiIlliwBERkSwZNeCio6MxcOBA9O7dG0FBQQCAI0eOQK1Ww93dHaGhocZcPBERVWJGC7jk5GTMmjULq1evRkREBP78808cOnQIAQEBWL16NSIjI3Hu3DkcOnTIWCUQEVElZrSA279/Pzw8PFC7dm2Ym5sjNDQUVatWRf369VG3bl2YmZlBrVYjKirKWCUQEVElZrSbLSclJcHc3ByjR49GWloa3Nzc0KRJE9SqVUuax97eHikpKcYqgYiIKjGjBZxWq8WJEyewYcMGVKtWDRMmTEDVqlWLzKdQKF6o3ZJuqklERPQ0owWcnZ0dHB0dYWtrCwB49913ERUVBZVKJc2TmpoKe3v7F2qXowkQEVEBk4wm4Obmhri4ONy/fx9arRaHDx9G7969ce3aNSQlJUGr1WLPnj1wcXExVglERFSJGa0H17ZtW3zwwQfw8fFBbm4unJyc8J///AcNGzbEpEmTkJ2dDVdXV/Tu3dtYJRARUSXGAU+JiKjC4oCnRERU6RjtEGVlEh8fi7g44/9gPTNTAwCwtrYx+rKcnV3h5MTzo0RUcbEHV4FkZmYiMzPT1GUQEVUIPAdXgQQHzwMATJsWaOJKiIjKB56DIyKiSocBR0REssSAIyIiWWLAERGRLDHgiIhIlhhwREQkSww4IiKSJQYcERHJEgOOiIhkiQFHVMFoNBlYsGCudG9SIioeA46ogomI2IlLly4gPDzM1KUQlWsMOKIKRKPJQFzcIQghEBcXy14cUSkYcEQVSETETulm4zqdjr04olIw4IgqkISEeGi1eQAArTYPCQnxJq6IqPxiwBFVII6OTlCp8scpVqnM4OjoZOKKiMovBhxRBaJWD4BSqQAAKJVK9Os30MQVEZVfDDiiCsTGpgacnV2hUCjg7OwCa2sbU5dEVG6ZmboAInoxavUA3Lp1k703Ij0YcEQVjI1NDfj7zzR1GUTlHg9REhGRLDHgiIhIlhhwREQkSww4IiKSJQYcERHJEgOOiIhkiQFHRESyxIAjIiJZYsAREZEsMeCIiEiWeKsuIgOJj49FXNwhoy+nYBTvsrjRsrOzK5ycXIy+HCJjYA+OqILJzMxEZmamqcsgKvfYgyMyECcnlzLp7QQHzwMATJsWaPRlEVVk7MEREZEsMeCIiEiWGHBERCRLDDgiIpIlBhwREckSA46IiGSJAUdERLLEgCMiIlliwBERkSwx4IiISJYYcEREJEsMOCIikiUGHBERyRIDjoiIZIkBR0REssSAIyIiWWLAERGRLDHgiIhIlhhwREQkSww4IiKSJQYcERHJEgOOiIhkiQFHRESyxIAjIiJZYsAREZEsMeCIiEiWGHBERCRLDDgiIpIlBhwREckSA46IiGTJzJiNDx8+HOnp6TAzy1/M3LlzcePGDXz99dfIzc2Fr68v3n//fWOWQERElZTRAk4IgatXryImJkYKuJSUFEyePBlhYWGwsLDAkCFD0LVrVzRu3NhYZRARUSVltIC7evUqFAoFxowZg/T0dPzrX//Ca6+9hm7dusHGxgYA0KtXL0RFRWHixInGKoOIiCopo52Du3//PhwdHbFq1SqsW7cOmzdvxu3bt1GrVi1pHnt7e6SkpBirBCIiWdNoMrBgwVxkZmpMXUq5ZLQeXPv27dG+fXsAQLVq1eDt7Y358+dj/PjxheZTKBQv1G7NmlYGq7GiMTdXAQBq1apu4krIlLgfUIFt2zbg0qUL2L9/Dz788ENTl1PuGC3gTpw4gdzcXDg6OgLIPydXp04d3L17V5onNTUV9vb2L9RuenoWdDph0ForitxcLQAgLe2BiSshU+J+QEB+7+3AgQMQQmD//gN47z1PWFvbmLqsMqdUKkrs+BjtEOWDBw8QEhKC7OxsZGVlYefOnVi4cCESEhJw7949PH78GL/++itcXFyMVQIRkWxFROyUvuzrdDqEh4eZuKLyx2g9ODc3N5w+fRr9+/eHTqeDj48POnbsiMmTJ2P48OHIzc2Ft7c32rRpY6wSiIhkKyEhHlptHgBAq81DQkI8hg0bZeKqyhej/g7uk08+wSeffFJomlqthlqtNuZiiYhkz9HRCbGxMdBq86BSmcHR0cnUJZU7vJMJEVEFpFYPgFKZf5GeUqlEv34DTVxR+cOAIyKqgGxsasDZ2RUKhQLOzi6V8gITfYx6iJKIiIxHrR6AW7dusvdWAgYcEVEFZWNTA/7+M01dRrnFQ5RERCRLDDgiIpIlvQE3adIkHDlypCxqISIiMhi9Aefu7o7Vq1ejV69e+OGHH6DRaMqgLCIiolejN+DUajV+/vlnrF69Gunp6Rg8eDCmTJmCM2fOlEV9REREL+W5zsHpdDokJSXh+vXryMvLQ82aNTF79mwsXLjQ2PURERG9FL0/EwgNDUVYWBjq1q0LHx8fLFu2DObm5nj06BHc3NwwZcqUsqiTiIieodFk4JtvVuDDDz/iD72LoTfg7t27h++++w7NmzcvNL1atWpYvHix0QojIqLSRUTsxKVLFxAeHsYbLRdD7yFKPz8/bN68GQBw9epVTJgwAWlpaQAAZ2dn41ZHRETF0mgyEBd3CEIIxMXFclTvYugNOH9/fzRs2BAAUKdOHXTp0gUBAQFGL4yIiErG8eD00xtwGRkZGD58OACgSpUq8PX1lXpwRERkGsWNB0eF6Q04rVaLlJQU6fHdu3chhDBqUUREVDpHRyeoVPmXUXA8uOLpvcjE19cX/fv3xzvvvAOFQoEjR45g6tSpZVEbERGVQK0egLi4Q9BqOR5cSfQGnLe3N1q3bo2jR49CpVJh9OjRaNq0aVnURkREJSgYDy4m5iDHgyvBcw2XU716dXTp0gVCCOTm5uL8+fNo1aqVsWsjIqJScDy40ukNuIULF+Lnn39GzZo1pWkKhQIHDx40amFERFQ6jgdXOr0Bt3fvXvz6669wcHAoi3qIiIgMQu9VlG+88QbDjYiIKhy9PThHR0eEhITg3XffhaWlpTSd5+CIiKg80xtwYWH5v46PioqSpvEcHBERlXd6Ay46Oros6iAiIjIovefgHj58iLlz52LEiBHQaDSYOXMmHj58WBa1ERERvTS9ARcUFITq1asjPT0dVapUQVZWFmbO5GWpRERUvukNuMTEREyePBlmZmaoWrUqFi1ahMTExLKojYiI6KXpDTilsvAsWq22yDQiIqLyRu9FJp07d8bChQvx5MkTHD58GBs3bkTXrl3LojYiIqKXpjfgPv/8c3z77beoXr06QkND8c4772DChAllUdsr27TpJyQnJ5m6DIO5cSN/XYKD55m4EsOpW7c+fHyGm7oMIpIhvQFnbm4OPz8/+Pn5lUU9BpWcnIQLly5DZWlj6lIMQqdVAQAuJ981cSWGoX2iMXUJRCRjJQbcxx9/jGXLlkGtVhf7fEREhNGKMiSVpQ2q1X/X1GVQMR4l8WYBRGQ8JQbcmDFjAACBgYFlVgwREZGhlHg5ZOvWrQEA9erVQ2RkJLp06QI7OzusW7cODRo0KLMCiYiIXobe6/39/f3RsGFDAECdOnXQpUsXBAQEGL0wIiKiV6E34DIyMjB8eP5VblWqVIGvry/S0tKMXhgREZVOo8nAggVzkZmpMXUp5ZLegNNqtUhJSZEe3717F0IIoxZFRET6RUTsxKVLFxAeHmbqUsolvT8T8PX1Rf/+/fHOO+9AoVDgyJEjmDp1alnURkREJdBoMhAXdwhCCMTFxaJfv4GwtrYxdVnlit6A8/b2RuvWrXH06FGoVCqMHj0aTZs2LYvaiIioBBERO6HT5R9N0+l0CA8Pw7Bho0xcVflS4iHKK1euAADOnz8PrVaLzp07o0OHDsjNzcX58+fLrEAiIioqISEeWm0eAECrzUNCQryJKyp/SuzBhYSEYM2aNZg0aVKR5ziiNxGRaTk6OiEm5iCEEFAoFHB0dDJ1SeVOiQHXokULAPlB16lTpzIriIiI9HN17YHffjsAABBCoHt33rHpWSUeotyzZw9SUlIwd+5cZGZmQqPRFPpHRESmc+hQNADF/z1SICaGR9WeVWIPzsnJCd27d4cQosjwOAqFgoOeEhGZUP45t4KfbAkkJMTzIpNnlNiDmzNnDhITE9GxY0f89ddfhf4x3IiITMvR0QkqVX4fRaUy4zm4Yuj9mcDGjRtx+vRpHD58GLm5uXB2dkbnzp3LojYiIiqBWj0AcXGHoNUCSqUS/foNNHVJ5Y7eO5ns3r0bH330ETIzM/Hw4UN8+umn2Lp1a1nURkREJbCxqQFnZ1coFAo4O7vwR97F0NuDW7t2LbZt2wZ7e3sA+cPojB49Gv/617+MXhwREZVMrR6AW7dusvdWAr0Bp9PppHADAAcHByiVejt+RERkZDY2NeDvP9PUZZRbepPKxsYGBw4ckB4fOHAA1tbWRi2KiIjoVentwc2cORMffvgh5s2bBwAwNzfHqlWrjF4YERHRq9AbcNevX0dUVBSuX78OnU6HBg0awMxM78uIiIhMSu8hytDQUKhUKjRq1AhNmjRhuBERUYWgN62aNm2Kr7/+Gp06dUK1atWk6a1atTJqYURERK9Cb8CdPn0ap0+fxrZt26RpHE2AiIjKO70BFx0dXRZ1EBnNpk0/ITk5ydRlGMyNG/nrEhw8z8SVGFbduvXh4zPc1GWQjOgNuIcPH2LVqlWIi4uDSqVCjx49MG7cOFhYWJRFfa8kM1MD7RMNHiWxt1keaZ9okJlp/HO6yclJuH75L9S2ksf542rQAQCe/H3ZxJUYzt9ZeaYugWRI71/8jBkzoFQqMX36dAghsHXrVgQFBWHu3LllUR+RQdS2MsPINramLoNKsPbMPVOXQDKkN+D+/PNP7Nu3T3rcrVs39O3b16hFGYq1tQ3S7uehWn0OBFgePUo6yPvnEZHR6P2ZgJ2dHe7d+//frh49eoQaNWoYtSgiIqJXpbcHZ2dnh0GDBqF3795QqVQ4ePAg7OzsEBQUBCD/ECYREVF5ozfgmjRpgiZNmkiPK8rhSSIiqtz0BtzEiRPLog4iIiKD4rg3REQkSww4IiKSJb0Bl5KSUmTa5cvy+YEpERHJU4kBp9FooNFoMGbMGGRmZkqP7969iwkTJjz3AoKDg+Hv7w8ASExMxKBBg9CrVy988cUXyMvj3QuIiMg4Sgy4zz77DN26dcPFixfRtWtXdOvWDd26dUP37t3RsmXL52o8ISEBO3fulB5PmTIFgYGB2Ldvn3RXFCIiImMoMeB++OEH/PXXXxgwYAD++usv6d+5c+ewdOlSvQ1rNBqEhoZi/PjxAIBbt27hyZMnaNeuHQBg4MCBiIqKMshKEBERPUvvzwTmz5+PW7duITMzE0IIabq+8eBmzpyJyZMn486dOwCA1NRU1KpVS3q+Vq1axZ7f06dmTavnntfcXPXC7VPZMjdXoVat6kZfxhOjLoEMoSz2Bapc9AbcokWLsGHDBtSsWVOapm88uG3btuGNN96Ao6MjwsLCAKBQOD7dzotKT8+CTle0reLk5mpfuH0qW7m5WqSlPTD6Mqj8K4t9geRHqVSU2PHRG3CRkZH49ddf4eDg8NwLjIyMRFpaGry8vJCZmYlHjx5BoVDg7t270jxpaWmwt7d/7jaJiIhehN6Ae+ONN14o3ABg7dq10v/DwsLw+++/Y/78+fD09MTJkyfRsWNH7Nq1Cy4uLi9eMRER0XPQG3COjo4ICQnBu+++C0tLS2m6vnNwxVm0aBFmzJiBhw8fomXLlhg+nKP3EhGRcegNuIJzaE9f8ajvHNzTBg4ciIEDBwIAmjdvju3bt79MnURERC9Eb8BFR0eXRR1EREQGpfdWXQ8fPsTcuXMxYsQIaDQazJw5Ew8fPiyL2oiIiF6a3oALCgpC9erVkZ6ejipVqiArKwszZ84si9qIiIhemt5DlImJiZg/fz4OHTqEqlWrYtGiRfD09CyL2ogMIjNTg4ysPKw9c8/UpVAJ/s7KQ41MjanLIJnR24NTKgvPotVqi0wjIiIqb/T24Dp37oyFCxfiyZMnOHz4MH7++Wd07dq1LGojMghraxtUeXwXI9vYmroUKsHaM/dgaW1j6jIMJj4+FnFxh4y+nMz/6/Val8F75+zsCienivXbZb1dsc8//xzVqlVD9erVERoaiubNm2Pq1KllURsREZUiMzMTmZmZpi6j3NLbgzM3N0eXLl3g5+cHjUaDEydOoEqVKmVRGxFRheTk5FImvZ3g4HkAgGnTAo2+rIpIbw8uNDQUy5cvBwA8efIE3377LVavXm30woiIiF6F3oA7ePAgfvzxRwBA7dq18fPPPyMyMtLohREREb0KvQGXm5sLc3Nz6bG5uflLDXNDRERUlvSeg+vQoQM+++wzeHt7Q6FQYNeuXWjbtm1Z1EZERPTS9AZcYGAgli9fjvnz58PMzAyOjo6YOHFiWdRGRET00vQG3Ndffw1/f/+yqIWIiMhg9J6Di4mJKYMyiIiIDEtvD+6tt97CqFGj0KFDB7z22mvS9JEjRxq1MCIiolehN+BsbGwAALdu3TJ2LURERAajN+Dmz58PALh//z5ef/11oxdERERkCHrPwV27dg19+/ZF3759kZKSgj59+uDKlStlURsREdFL0xtw8+bNQ0BAAGrWrAkHBwcMHTqUA54SEVG5pzfgNBoNnJycpMfvv/8+srKyjFoUERHRq3qukUuzs7Ol23OlpaVBp9MZtSgiIqJXpfciEx8fH4wePRrp6elYvHgxfvnlF3zwwQdlURsREdFL0xtw3t7eqF+/PmJiYpCXl4e5c+fC2dm5LGojIiJ6aaUG3MWLF3H9+nW0bdsWU6ZMKauaiIiIXlmJ5+B27NiBoUOH4rvvvkO/fv0QFxdXlnURERG9khJ7cBs2bEBERAQcHBxw6tQphIaG8tAkERFVGKVeReng4AAAaN++PTIyMsqkICIiIkMoMeCeHbVbpVIZvRgiIiJDea7fwQFFA4+IiKg8K/Ec3IULF9ChQwfp8ZMnT9ChQwcIIaBQKPDHH3+USYFERIayadNPSE5OMnUZBnPjRv66BAfPM3ElhlO3bn34+Aw3SFslBtz+/fsNsgAiovIiOTkJF69egMrawtSlGIROpQUAXEm/ZuJKDEObmWPQ9koMuDp16hh0QURE5YHK2gLWLm+augwqRmbsbYO2p/dOJhWd9okGj5IOmroMg9DlPQEAKM0sTVyJYWifaADYmboMIpIpWQdc3br1TV2CQRUcb69XVy6hYCe7bURE5YesA85QJyrLi4ITydOmBZq4EiKi8u+5fyZARERUkTDgiIhIlhhwREQkSww4IiKSJQYcERHJEgOOiIhkiQFHRESyxIAjIiJZYsAREZEsMeCIiEiWGHBERCRLDDgiIpIlBhwREckSA46IiGSJAUdERLLEgCMiIlliwBERkSwx4IiISJYYcEREJEtmpi6AqCz8nZWHtWfumboMg8jK0QEArCzk8/3076w8/MPURZDsMOBI9urWrW/qEgwq9UYSAMCutnzW6x+Q33Yi02PAkez5+Aw3dQkGFRw8DwAwbVqgiSupeDIzNcjTZCMz9rapS6Fi5GmykWmmMVh78jnGQURE9BT24Iio0rC2tsHdvAxYu7xp6lKoGJmxt2FtbWOw9tiDIyIiWWLAERGRLDHgiIhIlhhwREQkSww4IiKSJQYcERHJklEDbtmyZfDw8EDfvn2xdu1aAMCRI0egVqvh7u6O0NBQYy6eiIgqMaP9Du7333/H0aNHER4ejry8PHh4eMDR0REBAQHYsGED3njjDYwbNw6HDh2Cq6urscogIqJKymg9uC5duuCnn36CmZkZ0tPTodVqcf/+fdSvXx9169aFmZkZ1Go1oqKijFUCERFVYka9k4m5uTmWL1+OH3/8Eb1790Zqaipq1aolPW9vb4+UlJQXarNmTStDl1lhmJurAAC1alU3cSVkStwPXl7Be0fll7m5ymD7ttFv1fXRRx9hzJgxGD9+PK5fv17keYVC8ULtpadnQacTBqquYsnN1QIA0tIemLgSMiXuBy+v4L2j8is3V/tC+7ZSqSix42O0gLty5QpycnLQokULVK1aFe7u7oiKioJK9f+/QaWmpsLe3t5YJRARFaHNzJHNaAK6J/mBrbSUR89Um5kD1DRce0YLuJs3b2L58uX473//CwA4ePAghgwZgpCQECQlJeGtt97Cnj17MGjQIGOVQERUiNzGnLvxf2MD1qspk/WqadhtZLSAc3V1xenTp9G/f3+oVCq4u7ujb9++sLW1xaRJk5CdnQ1XV1f07t3bWCUQERXCsQErF6Oeg/voo4/w0UcfFZrm6OiI8PBwYy6WiIiIdzIhIiJ5YsAREZEsMeCIiEiWjP47uMogPj4WcXGHjL6cgiumCk4sG5OzsyucnFyMvhwiImNhwFUg1tbWpi6BiKjCYMAZgJOTC3s7RETlDM/BERGRLDHgiIhIlhhwREQkSww4IiKSJQYcERHJEgOOiIhkiQFHRESyxIAjIiJZYsAREZEsMeCIiEiWGHBERCRLDDgiIpIlBhwREckSA46IiGSJAUdERLLEgCMiIlliwBERkSwx4IiISJYYcEREJEsMOCIikiUGHBERyRIDjoiIZIkBR0REssSAIyIiWWLAERGRLDHgiIhIlhhwREQkSww4IiKSJQYcERHJEgOOiIhkiQFHRESyxIAjIiJZYsAREZEsMeCIiEiWGHBERCRLDDgiIpIlBhwREckSA46IiGSJAUdERLLEgCMiIlkyM3UBRERyEx8fi7i4Q0Zfzo0bSQCA4OB5Rl+Ws7MrnJxcjL4cQ2LAERFVUNbW1qYuoVxjwBERGZiTk0uF6+3IEc/BERGRLDHgiIhIlhhwREQkSww4IiKSJQYcERHJEgOOiIhkiQFHRESyxIAjIiJZYsAREZEsMeCIiEiWGHBERCRLDDgiIpIlBhwREckSA46IiGSJAUdERLLEgCMiIlliwBERkSwZNeBWrlyJvn37om/fvggJCQEAHDlyBGq1Gu7u7ggNDTXm4omIqBIzWsAdOXIEcXFx2LlzJ3bt2oXz589jz549CAgIwOrVqxEZGYlz587h0KFDxiqBiIgqMaMFXK1ateDv7w8LCwuYm5ujUaNGuH79OurXr4+6devCzMwMarUaUVFRxiqBiIgqMTNjNdykSRPp/9evX0dkZCSGDRuGWrVqSdPt7e2RkpLyQu3WrGllsBqJKiJzcxUAoFat6iauhKh8M1rAFbh06RLGjRuHadOmwczMDNeuXSv0vEKheKH20tOzoNMJQ5ZIVKHk5moBAGlpD0xcCZHpKZWKEjs+Rr3I5OTJk/D19cVnn32GAQMGwMHBAXfv3pWeT01Nhb29vTFLICKiSspoAXfnzh34+flh0aJF6Nu3LwCgbdu2uHbtGpKSkqDVarFnzx64uLgYqwQiIqrEjHaI8ocffkB2djYWLFggTRsyZAgWLFiASZMmITs7G66urujdu7exSiAiokpMIYSoUCe0eA6OKrvg4HkAgGnTAk1cCZHpmewcHBERkamwB0dkIPHxsYiLM/6NC27cSAIA1KtX3+jLcnZ2hZMTz5NT+VVaD87oPxMgIsOytrY2dQlEFQJ7cEREVGHxHBwREVU6DDgiIpIlBhwREckSA46IiGSJAUdERLLEgCMiIlliwBERkSwx4IiISJYYcEREJEsMOCIikiUGHBERyRIDjoiIZIkBR0REssSAIyIiWapw48EplQpTl0BEROVEaZlQ4caDIyIieh48RElERLLEgCMiIlliwBERkSwx4IiISJYYcEREJEsMOCIikiUGHBERyRIDjoiIZIkBR0REssSAM5CLFy+iWbNm2LdvnzStR48euHnzZpF5S5puKMOGDTNYW1u3boWbmxuCg4MN1mZ5dfPmTTRr1gzx8fGFphdsr4cPH2LOnDl477330K9fP/j4+CAhIQEAcOHCBXh5ecHLywtdunRB9+7d4eXlhcGDBxdq42nDhg3DsWPHAAC5ubkIDQ2Fu7s71Go1vL29ERkZKc27YsUKNGvWDKdOnSrUxpdffolmzZoZ9H2Ijo7G2rVrDdJWVlYWBg4cCC8vL1y7ds0gbZY3N2/eRI8ePYpML9gud+/exeeff46ePXtCrVZj9OjR+PPPP6X5hg0bhg4dOiAnJ6fQ6728vAz6twwAy5cvx4kTJwzS1vnz59GjRw+D12hIFe5elOVVWFgYevXqhc2bN6NXr14mreX33383WFt79uzBvHnz4OzsbLA2yzNzc3MEBgYiPDwcVlZW0nQhBMaPH48WLVrgl19+gYWFBf7880+MHTsWixcvRteuXbF7924AgL+/P7p06YKBAwc+93IDAwORnZ2NsLAwWFlZITk5GWPGjEFOTg769+8PAKhduzb27duH9u3bAwB0Oh2OHz9uuJX/P+fPnzdYW4mJibCwsMDmzZsN1mZF8uTJEwwfPhyDBg3CwoULoVAoEB8fj1GjRmHTpk1o2LAhAKB69eqIi4uTgvLq1atITU3F66+/btB6jh8/jq5duxqkrd9++w2enp749NNPDdKeMTDgDCAvLw/h4eHYuHEjhgwZghs3bqBevXoGa3/YsGFo2LAhzpw5g+zsbAQEBMDZ2Rm3bt3C9OnTce/ePVhaWiIoKAjbt28HAAwePBjbtm1Dt27d4ObmhnPnzuG1117DokWL8NZbb6FHjx5o06YNEhMTsWnTJsTExGDt2rVQKBRo1aoVAgMDsXbtWpw9exZz5szBjBkz4OrqarB1Kq/s7e3x9ttvIzg4GPPmzZOmHz9+HLdv38ZPP/0EhSL/5q4tW7bEhx9+iNWrV7/Sh0ZycjL27duH+Ph4VKtWDQBQt25dTJ8+HfPmzZMC7t1330V0dDT8/f0BACdPnkS7du2QmJhYavvHjh3DihUrYGZmhjt37qBNmzb48ssvYWFhgXXr1uG///0vVCoV3NzcMGDAACmM3nzzTdy+fRvXr1/HjRs3oNFo8O9//xsffPABwsLCsHPnTmg0Gri5uWH48OH44osvcPv2bZiZmWHy5Mlo1aoVAgICcPfuXYwfPx7ffPPNS79HFVVkZCRq1qyJ0aNHS9OcnJwwcOBAfP/99/jqq68AAO7u7ti3b58UcJGRkejVqxeuXLlSavsrVqwodvtkZ2djzpw5OHnyJMzNzTFhwgTk5OTg3LlzmDFjBlauXImgoKBiP1f8/f2h0WiQlJSEKVOmwNbWFl9++SWys7NRo0YNzJ07F9evX8d///tfAICFhQUmTpxopHfw1TDgDCAmJgZvvvkmGjRogJ49e2Lz5s2YOnWqQZeRk5ODnTt3IjExEWPGjEF0dDTmzJmDXr164f3338ehQ4fw9ddfY9myZdiwYQO2bdsGAMjIyECXLl0wf/58bNiwAUFBQdIHjYuLC5YuXYoLFy7gm2++wdatW1GjRg3MmTMHK1euxLRp03Ds2DFMnDjRYN/6KgJ/f3+o1WrEx8fDyckJAHDv3j20bt1aCrcCnTt3xuLFi5+r3bFjx8Lc3Fx6fOPGDQDAuXPn0KhRIyncCnTq1AnJycnQaDQAgBo1auCtt97CmTNn0KZNG0RGRsLDw0P6oCnNmTNnsGvXLjRo0AAff/wxNm7ciI4dO2LTpk3YsWMHqlatig8++AB9+vTBkCFDAACDBg3CihUrcPHiRWzevBk6nQ4DBw6Eo6MjACAlJQWRkZEwMzPDxx9/jG7dumHkyJFITk7Gf/7zH+zatQtBQUFYuXKl7MMtNTUVXl5eRaafPXsW//znP4tM79y5M5YsWSI9fueddzBr1izk5ubC3NwcMTExmDRpkt6AA1Ds9klISMCjR4+wd+9epKenw9fXFzt37sSOHTswceJE6fBpcZ8rAGBjY4NvvvkGOTk56N27N5YuXYo2bdpg7969+PTTT7Fjxw5pPymv4QbwHJxBhIWFwdPTEwDg4eGBnTt3Fjme/qr+9a9/AQBatGiBWrVq4cKFCzh+/Lj0R+Xq6oply5YVeV2VKlWkHsCAAQOkcz4A0LZtWwD5vRM3NzfUqFEDAPDvf/8bR48eNWj9FYmVlRXmzZuHwMBAZGVlAQAUCgW0Wm2ReXNzc5+73W+//Ra7d++W/rVu3fqF2+7Tpw/27dsHrVaLU6dOoVOnTs+17M6dO6Nhw4ZQKBTw8vLC0aNHpe1evXp1mJmZYd26dVJNT/P09MRrr72G6tWro0ePHtK+0bJlS5iZ5X9HPnr0KLy9vQHk9z7btm2L06dPP98bIwP29vaFtm3B4ern3bZVqlRBx44dceTIEVy8eBF169aFpaXlcy27uO1z/PhxqNVqKJVK1KpVSzqs/qziPlcAoE2bNgCA69ev4/XXX5ce9+nTBzdu3MCDBw+e850xLfbgXlF6ejpiY2Nx7tw5/PTTTxBC4P79+/j1118NuhyVSiX9X6fTwczMTPpwAfLPEV25cgWNGzcu9DqlUin1OnQ6XaF2qlSpIk1/mhACeXl5Bq2/onF2dpYOVQL5XwY2bNggfcMu8L///a/Yb+gvok2bNrh+/ToyMzNhbW1dqO26devCxsZGmtazZ0/85z//gbOzMzp16gSl8vm+oz693YUQUKlUhfYfIL9HVrVq1VJf+/Q+9PQH8LOjbgkhiv1gr2zatGlTbA/71KlTRfab3r17Y9++fXBwcICHh8dzL6O47fPstk1KSsIbb7yh97UFryvYts9+NgAVa9uyB/eKwsPD0a1bN8TGxiI6Ohq//fYbxo8fjy1bthh0OQVX1J09exb3799H06ZN0alTJ/zyyy8AgCNHjiAwMBBA/k5bEFCPHz+WDjuEhYXBxcWlSNtdunRBdHS0dChs69atleqQZEn8/f0RFxeH1NRU1K5dG40bN8ZXX30lffs+d+4cvv76a0yYMOGVlvPmm29CrVbjiy++wMOHDwHkH76cP39+kcM/NWrUQJ06dbBs2bIX+hA8efIkUlJSoNPpsGvXLri4uKBTp06IjY3Fw4cPkZeXh88++wznzp0rtP8AwIEDB5CTk4PMzEz89ttvxV5w1K1bN+n8b3JyMv744w+0a9fuJd4NefHw8MDjx4+xZs0a6UtAXFwcwsLCCp2XA/JPGRw7dgyxsbHF/p2WpLjt07lzZ+zduxdCCKSnp2Po0KHIycmBSqUqFE7Ffa48rWHDhtBoNDhz5ow0/5tvvlnoS1d5xh7cKwoLC8PkyZMLTfPx8cH3338PKysreHp6FjpvU3CZd0nTS5KcnIwBAwYAAEJDQ6FSqTBz5kzMmDEDmzZtQtWqVREUFAQg/2IELy8vhIWFAQCioqIQGhoKe3v7Yi/3b968OcaNG4dhw4YhNzcXrVq1wpw5c17i3ZCXgkOVBR9EK1euRGhoKDw9PaFSqWBtbY2FCxca5MvArFmzsGbNGnh7e0OpVKJKlSr4+OOPiw2x3r17Y9WqVdLVlM/D3t4eU6dORUpKCpycnDB48GCoVCoMHToUQ4YMgU6nw3vvvYe3334b5ubmmDZtGuzs7ADk9/R9fHyQlZWFcePGoXHjxtIHXoEvvvgCM2fOlPa5oKAg2Nvby/anAc/LwsIC69evR0hICHr37g2FQoE333wTa9euRaNGjYrM26FDBwD//+jK8yhu+9SrVw9BQUHo168fgPyrdK2srKRzfQWfA8V9rjxbU2hoKObNm4fHjx/D2toaoaGhL/1+lDWO6F0BDBs27KUv9GjWrJl0XJ0qp2PHjmHlypXYsGHDC792xYoVAIBJkyYZuiwygFfZPq/yuVJRsAdXTnz22We4fPlykenF/YCU6FknTpwo9LOGp33wwQdlXA0Z0rp167Bz584i0+3t7aWLP6h47MEREZEs8SITIiKSJQYcERHJEgOOiIhkiQFHlYZWq8XatWulu9t7eHhg4cKFz3XXmZUrV+LAgQNlUOWru3PnDjw9PdGvXz+9Pz8psG3bNmzcuPGFl+Xp6Vno7jgvKiYmptg78BAZAgOOKo3Zs2fj1KlTWL9+PXbv3o3t27fj2rVr+OKLL/S+9tixYxXm7i7Hjh2DnZ0dwsPDn/u3cidPnsSTJ0+MXFlRZ8+eRWZmZpkvlyoH/kyAKoXk5GREREQgLi5OGganWrVqmDNnjtTLuXbtGubOnYtHjx4hNTUVzZs3x9KlS7F9+3acO3cOISEhUKlUcHV1xaJFi3D8+HFotVq0bNkSM2bMgJWVFc6cOYPZs2cjNzcX9erVw+3bt+Hv74+uXbtiy5Yt2LBhA5RKJezs7BAYGIgGDRpId29PTk6Gi4sLtm/fjq1bt6JBgwYAgJEjR+L9999Hz549C61Tce2lpKRg6dKlePDgAYYNG1bkt2+bNm3C5s2bYW5ujipVqmDu3Lm4du0aoqOjER8fD0tLS9y7dw8ZGRmYOXMmgPzfWhU8vnz5MgICAvD48WM0bNgQjx49ktr+448/sGjRIjx+/BgKhQKTJk2Cm5sbwsLCsH//fiiVSiQlJcHc3BzBwcF4/PgxNm/eDK1Wi+rVqxe5YQLRKxNElUBUVJQYNGhQqfMsWLBA7Nq1SwghRE5OjvD09BRRUVFCCCGGDh0q9u7dK4QQYsWKFWLBggVCp9MJIYRYvHixmDVrlsjNzRUuLi4iJiZGCCFEQkKCaNasmTh69Kg4cuSI6Nmzp0hPTxdCCLFjxw7Rp08fodPpxLRp08SIESOkOoKCgkRwcLAQQoikpCTh6uoq8vLyCtVaWns7duwQY8eOLbJ+eXl5olWrViIlJUUIIcTOnTvF5s2bhRBCTJs2TXz//fdCCCGWL18u5syZI73u6cdeXl5i69atQgghTpw4Ia2fRqMR7u7uIjk5WQghxN9//y1cXFzErVu3xI4dO0THjh3FnTt3hBBCzJ07V0ydOrXYZREZEntwVCkolcpibxz7tClTpiA+Ph7fffcdrl+/jtTU1EI9lAIxMTF48OABjhw5AiD/zvA1a9bExYsXAUAaN69bt25o0qQJAODw4cPw8PCAra0tAGDgwIH48ssvpVG+O3bsKLXv4+ODoUOHYvLkydiyZQu8vb2L3EJJX3vFUalU6N27N4YMGYLu3bvDyckJarW61PfkaRkZGbhw4YI0OkXHjh2l9fvf//6HtLQ0+Pn5SfMrFArpLjqtWrVC7dq1AeSPQrB///7nXi7Ry2LAUaXQpk0bXL16FVlZWYVG6k5JSUFgYCCWL18Of39/aLVa9OnTB927d8edO3eK3CUfyL/DekBAgBRkDx8+RHZ2NtLS0orMXxBMxbUjnhq14emx4Bo0aIBmzZrh4MGDiIiIkMb2e/a1pbVXkkWLFuHixYs4cuQIvvvuO2zfvh1ff/11oXkUCkWh9gtuLl1w79Snnyu4+7xWq0WjRo0K1ZqSkgJbW1tEREQUGnng2faJjIUXmVCl4ODgALVajYCAAGmMt6ysLMyePRs2NjawtLREXFwc/Pz84OHhAYVCgdOnT0t3Xn/6DvvOzs7YuHEjcnJyoNPpEBgYiCVLlqBRo0awsLBAbGwsgPxBRi9evAiFQgFnZ2dERkbi3r17AIAdO3bAxsYG9evXL7ZeHx8fhISEoG3btnBwcCjy/Iu2B+QP2urq6gobGxv4+vrik08+kXpYT69fjRo1cP78eQgh8OjRI8TFxQHIHwSzVatWUoidP39e6rW2a9cOSUlJOH78OAAgMTERvXr1Qmpqaqnb5dmRC4gMiT04qjRmzZqF1atXY8iQIVCpVMjJyUHPnj2lG9VOnjwZfn5+sLa2RtWqVdG5c2dp1G03NzcEBwcjNzcXEyZMQHBwMAYMGACtVosWLVrA398fZmZmWLFiBWbNmoUlS5bgH//4B+zs7GBpaYkuXbrA19cXI0aMgE6ng62tLdasWVPieG5ubm6YMWOGNGrys5ycnF6oPQCwtbXFhx9+CF9fX1haWkKlUkkjULi4uEj3svTx8cHhw4fh7u4OBwcHtG/fXupxLVmyBNOnT8fmzZtRr149NGzYUGp7+fLlCAkJQXZ2NoQQCAkJQZ06dUrdJo6Ojpg0aRLMzc2l4Z6IDIX3oiQyoODgYIwePRp2dna4c+cOvLy8cODAAbz++usv1M4ff/yBwMBA7Nmzp9CwSkT0/NiDIzKgOnXqwNfXF2ZmZhBCICgo6IXDbdq0afj9998RHBzMcCN6BezBERGRLPEiEyIikiUGHBERyRIDjoiIZIkBR0REssSAIyIiWWLAERGRLP0/wmckR2OAzMwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot( data=mean_state_l)\n",
    "plt.ylabel(\"Percent proficiency\")\n",
    "plt.xlabel(\"Category of student\")\n",
    "plt.title(\"Category of Student vs % Prof RLA Boxplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are quite a few more outliers in this data set than the previous 2. Similar to both, the next few blocks of code investigates and handles them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          ALL_pctprof  NOTHOM_pctprof  HOM_pctprof\n",
      "STNAM                                             \n",
      "VIRGINIA         65.0       62.339286    47.491071 \n",
      "\n",
      "           ALL_pctprof  NOTHOM_pctprof  HOM_pctprof\n",
      "STNAM                                             \n",
      "IOWA        59.066667       57.000000    35.066667\n",
      "VIRGINIA    65.000000       62.339286    47.491071 \n",
      "\n",
      "              ALL_pctprof  NOTHOM_pctprof  HOM_pctprof\n",
      "STNAM                                                \n",
      "KENTUCKY       54.737143       49.045714    43.474286\n",
      "LOUISIANA      58.896226       54.811321    49.594340\n",
      "PUERTO RICO    46.222222       40.296296    43.962963\n",
      "VIRGINIA       65.000000       62.339286    47.491071\n"
     ]
    }
   ],
   "source": [
    "#Find the upper outliers \n",
    "for cat in categories: \n",
    "    locals()['l_upper_outliers_' + cat] = mean_state_l[mean_state_l[cat] > lea_upper_whiskers[cat]]\n",
    "#Let's take a look at all these outliers \n",
    "print(l_upper_outliers_ALL_pctprof, '\\n\\n', l_upper_outliers_NOTHOM_pctprof, '\\n\\n', l_upper_outliers_HOM_pctprof)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using similar logic to that of the graduation outlier section, we've elected to cutout Iowa and Puerto Rico from the data sets. As seen in their \"which state has more influence\" graph, Iowa and Puerto Rico were relatively low on the scale, meaning they had less data, and were more likely to contain unreliable data. As for Virginia, Louisiana, and Kentucky, since they contained an ample amount of data we've elected to include them into the analysis for now. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_state_l.drop(index=['IOWA','PUERTO RICO'], inplace=True)\n",
    "\n",
    "# Recalculate the total row \n",
    "mean_state_l.loc['Total'] = mean_gradRates.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL_pctprof</th>\n",
       "      <th>NOTHOM_pctprof</th>\n",
       "      <th>HOM_pctprof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.914871</td>\n",
       "      <td>38.126958</td>\n",
       "      <td>32.317666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.158383</td>\n",
       "      <td>8.316404</td>\n",
       "      <td>5.226593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.659091</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.757892</td>\n",
       "      <td>31.487179</td>\n",
       "      <td>29.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.639344</td>\n",
       "      <td>37.822430</td>\n",
       "      <td>31.282609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>44.833082</td>\n",
       "      <td>41.737725</td>\n",
       "      <td>34.991858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>62.339286</td>\n",
       "      <td>49.594340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ALL_pctprof  NOTHOM_pctprof  HOM_pctprof\n",
       "count    47.000000       47.000000    47.000000\n",
       "mean     40.914871       38.126958    32.317666\n",
       "std       8.158383        8.316404     5.226593\n",
       "min      28.659091       19.000000    23.000000\n",
       "25%      33.757892       31.487179    29.600000\n",
       "50%      40.639344       37.822430    31.282609\n",
       "75%      44.833082       41.737725    34.991858\n",
       "max      65.000000       62.339286    49.594340"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_state_l.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown by the description above, nothing has really changed in terms of the description's metrics for the mean RLA data set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've cleaned up our data of outliers, let's plot the data into histograms to see if our homeless and non-homeless populations for all data sets takes on a normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms of Homeless and Non-homeless graduation rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Grad Rates for Non-homeless Students')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = mean_gradRates, x = mean_gradRates['NOTHOM_rate'], bins = 11)\n",
    "plt.axvline(x=mean_gradRates['NOTHOM_rate'].mean(),color='red', ls='--')\n",
    "plt.title(\"Histogram of Grad Rates for Non-homeless Students\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Grad Rates for Homeless Students')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = mean_gradRates, x = mean_gradRates['HOM_rate'], bins = 13)\n",
    "plt.axvline(x=mean_gradRates['HOM_rate'].mean(),color='red', ls='--')\n",
    "plt.title(\"Histogram of Grad Rates for Homeless Students\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the most part, both the homeless and non-homeless populations have normal distributions for the graduation rate data set. This means that we can use parametric tests to compare these two populations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms of homeless / nonhomeless students' proficiency in math by school"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of the Percent of Non-Homeless Students who were Proficient in Math by State')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = mean_state_m, x =mean_state_m['NOTHOM_pctprof'], bins=11)\n",
    "plt.axvline(x=mean_state_m['NOTHOM_pctprof'].mean(),color='red', ls='--')\n",
    "plt.title(\"Histogram of the Percent of Non-Homeless Students who were Proficient in Math by State\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of the Percent of Homeless Students who were Proficient in Math by State')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = mean_state_m, x =mean_state_m['HOM_pctprof'], bins=13)\n",
    "plt.axvline(x=mean_state_m['HOM_pctprof'].mean(),color='red', ls='--')\n",
    "plt.title(\"Histogram of the Percent of Homeless Students who were Proficient in Math by State\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, we can see that the homeless and non-homeless populations take on a mostly normal distribution for the math achievement data. Something to note is the tail towards the upper end of the data that is due to the outliers we identified in the section property. Nevertheless, this tail isn't significant and this means that these populations could also probably utilize parametric tests to test for differences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms of homeless / nonhomeless students' proficiency in math by school"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of the Percent of Non-Homeless Students who were Proficient in RLA by State')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = mean_state_l, x = mean_state_l['NOTHOM_pctprof'], bins=11)\n",
    "plt.axvline(x=mean_state_l['NOTHOM_pctprof'].mean(),color='red', ls='--')\n",
    "plt.title(\"Histogram of the Percent of Non-Homeless Students who were Proficient in RLA by State\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of the Percent of Homeless Students who were Proficient in RLA by State')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SS/muV9y9e7fQF2Ig/zXVvIpazy+//JKvf6ytrfMVtUePHuHWrVswGAxwdXU1XucDgDt37qBixYrYv39/viHa3PxFKezvQghkZ2ebdDCWZL1Awe2bt99zGQwGDBkyBH369AEAZGVlFXhBycnJQY0aNfDjjz8ap8XHx8PZ2RlarRYHDx5ETEwMTpw4ge7du2PZsmVo2rTpU6fnSkpKQlhYGKZNm4ZmzZqhWbNmGD58OKZOnYpt27YVKKpP9rVery90et7nKsW29PT0xN69exEdHY2YmBgsW7YM4eHhqFq1qnGecuXKoX79+jh06BAePHgAX19fLFu2DAcOHEDbtm2N+1lh+/6TxzqQfzvm1a5dOxw5cgTHjx/HypUrsWvXLkRGRsLe3h5Vq1Z95vPNu/+bsu1zPXkt8UlPrjcvg8Fg3FaFye2TvMfin3/+iVdffTXfOqQ8Jp9UkuX9/f1x7tw5fPLJJ9iyZUuhx1cuIYTxembum+KMjAycOnUKn3zyCcqXL59v/txLZbnL5q7bzc0NGRkZiImJwenTp7FgwYICbR08eBD379+Hv78/OnTogA4dOmDcuHF499138dtvv+Vr6++//0bPnj3Ro0cPvP322+jYsSMOHTpUYJ3FfX3OZZGP1Fy6dAne3t6oUaMGhg0bhgEDBuDy5csAHndk7oHUsmVLbNiwAUIIZGVlYdOmTXBzc0PTpk2N7ygAYO/evUhNTS30jOfYsWPo1q0bunfvjurVqyMqKgo5OTklzt6yZUtEREQY38msW7cO77zzToEX7yflfV5PU716ddjZ2Rk32p07d+Dt7W086zJVcdbj4uKCmJgYJCQkAADCw8OxcOFCtGjRAsePH8fVq1cBAIcPH0aXLl2QmZlZoueZu74bN24AeHyGfOfOHTRu3LhYz80c623ZsiU2b96MtLQ0AI/vUp84cWK+ed566y389ddf+O9//wvg8c1VHTp0QEJCAhYtWoTly5ejbdu2mDp1KmrWrIm4uLinTs+rXLlyiI6OxnfffWd8EcvIyMCdO3dQv359APn7tHz58sbtmJycjNOnTwMAateuDSEEDh8+DODxi0pucZBiW44fPx6RkZHw8vLCjBkz4OjoiDt37hRYpm3btvjiiy/g6uoKR0dHVK9eHatXr0aHDh2KbKt69erQarXYt28fgMdvWPbu3Qs3N7cC87Zr1w47d+5ETk4OKlasCHd3dyxcuNDYRnGerynbviRatWqF8PBw6PV6GAwGbNiwAe7u7k+d39HREQ0aNDB+OuLOnTvo3bs3Hjx4YJxH6mNSKuPHj0dCQgLWr19f5HzHjx9HUlISDhw4gKioKERFReHo0aPQ6XSF3jB18uRJ3L59GwDwww8/GK+9ajQa9OnTB1OnToW3tzfs7OwKLFu2bFl88cUX+e6avnnzJuzs7FC1alXY2NggJycHQghcuHABzs7O+Oijj9CqVStjQc3Jyck3X0lfny1yplq3bl106tQJ/v7+cHBwgL29vfEs1dPTEwsWLIBer0dQUBBmz54NHx8f6PV6tGrVCsOHD4etrS2++OILBAYGwsrKCm+++SZsbGyMwzB5DRo0CNOnT0dERASsra3RoEED/P777yXOHhAQgDt37qB79+4wGAyoVq0aFi1a9Mzl8j6vp7G1tcXy5csxZ84cfP3118jOzsaYMWPw9ttvFytjUes5efJkvnnr1KmDCRMmYMiQIQAAnU6HuXPnolKlSpg5cyY++eQT47vEFStW5Hs3XpjWrVsbh3Ly3hJfs2ZNzJgxA6NGjUJOTg7s7e3x1VdfPfNd3rNIsd7u3bsjPj4ePXr0gEajQeXKlTF//vx88zg7OyM0NBTBwcHIzMyEEALBwcGoUqUKPvjgA0yaNAne3t6wtbVFnTp14O3tjZSUlEKn52VjY4M1a9Zg4cKFWLduHRwcHKDRaNCtWzfjcFfefadfv3749NNP0aFDB7z22mto3rw5gMcjLMuWLcNnn32GL774AvXq1UOFChUAPL688Lzb8qOPPsLUqVOxceNGWFtbo23btsa282rbti1mzZqFTz/9FMA/b4yfdROLVqvF8uXLMXv2bISFhSEnJwcjR45EixYtCsybeyd07lB3y5YtsXz5cmNRLc7zNWXbl8SIESOwYMECdO3aFdnZ2WjUqBGmTZtW5DKLFy/G559/jnXr1kGj0WDOnDnQ6XTGv0uxHZ/3YyqFKVeuHD799FPMmzfPuH8vWbIES5cuNc7j6emJzMxM9OjRI9+xaWNjg2HDhiE0NBRDhgzJN2pVu3ZtTJkyBXfv3sUbb7yR76NY3bp1w4IFC9CzZ89CM7Vo0QLTpk1DYGAgHjx4AGtra+h0OixfvhzlypWDo6Mj6tevj06dOmHt2rWoVKkSOnbsiDJlyqBRo0ZwdnbGX3/9hWrVqhnn++GHH0r0+qwRxR0zkEFaWhqWL1+O0aNHo0yZMvj1118xbNgwHD169JnX54iIqHT7z3/+g61bt+Lrr7+WO8ozWeRM9Xk5OjpCq9UiICAANjY2sLGxwZdffsmCSkSkcP369cPdu3cRFhYmdxSTlIozVSIiotKA3/1LREQkERZVIiIiibCoEhERSYRFlYiISCKl4u7fvO7dewiDoXTcW1WhgiOSktLkjvFCkKsvnIYOBAA8WPWtxdt+Gu4X/2BfPMZ++Edx+8LKSoPy5cuaMVHxlLqiajCIUlNUAZSqrOYmS1/87/cUX7Tt8KLlkRP74jH2wz9Kc19w+JeIiEgiLKpEREQSYVElIiKSSKm7pkpUHOnjJsgdgYhUhEWVFE3v4Sl3BCJSEQ7/kqJZn4+F9flYuWMQkUrwTJUUzXHaJABAyrZImZMQkRrwTJWIiEgiLKpEREQSYVElIiKSCIsqERGRRHijEinawykz5I5ARCrCokqKlt3cRe4IRKQiHP4lRbM5dRI2p07KHYOIVIJnqqRoZed+DoCfUyUiy2BRpRJ7qVwZ2NmavgvpdE4lbiszKxupKRklXp6IyBJYVKnE7GxtMCHksEnzarU20OuzS9zWwjEeJV6WiMhSeE2ViIhIIiyqREREEuHwLyla2qz5ckcgIhVhUSVFy2nYSO4IRKQiHP4lRdMePgTt4UNyxyAileCZKimaw5KFAIAUD0+ZkxCRGvBMlYiISCIsqkRERBJhUSUiIpIIiyoREZFEeKMSKVraohC5IxCRirCokqLl1KwldwQiUhEO/5Ki2e7dDdu9u+WOQUQqwTNVUrQyK8IAAFkdOsmchIjUgGeqREREEmFRJSIikgiLKhERkURYVImIiCTCG5VI0R4sWyV3BCJSERZVUjRDldfkjkBEKsLhX1I0u21bYLdti9wxiEgleKZKimb/f2sAAJld/WVOQkRqYNYz1bS0NHh7e+PmzZsAgI0bN8Lb2xs+Pj6YPHkysrKyzNk8ERGRRZmtqJ47dw69e/dGXFwcAODatWtYs2YNwsPDsWPHDhgMBnz//ffmap6IiMjizFZUN23ahBkzZqBixYoAAFtbW3z22WdwdHSERqNB7dq1cfv2bXM1T0REZHFmu6Y6Z86cfI+rVKmCKlWqAACSk5OxYcMGzJs3z1zNExERWZzFb1SKj4/HkCFD4O/vDxcXl2IvX6GCoxlSmY9O5yR3BLPSak3fhYozb2FK1Jfbtz5e9l8v1nZQ+n5RHOyLx9gP/yjNfWHRonr16lV8+OGHeP/99zFo0KASrSMpKQ0Gg5A4mXnodE5ITHwgdwyz0emcoNdnmzSvVmtj8rxPU7K+tPvfwi/OdlD6flEc7IvH2A//KG5fWFlpXqiTLYsV1bS0NAwePBjjxo2Dr6+vpZollbML3wAAyOzVV+YkRKQGFvvyh82bN+Pu3bv45ptv4OvrC19fX4SEhFiqeVIp+/ANsP9fYSUiMjezn6lGRUUBAAYMGIABAwaYuzkiIiLZ8GsKiYiIJMKiSkREJBEWVSIiIonwC/VJ0VK+3yx3BCJSERZVUjYHB7kTEJGKcPiXFM3+m9Ww/2a13DGISCVYVEnR7HZshd2OrXLHICKVYFElIiKSCIsqERGRRFhUiYiIJMKiSkREJBF+pIYULWVbpNwRiEhFeKZKREQkERZVUrQyy0JRZlmo3DGISCVYVEnRbPfvge3+PXLHICKVYFElIiKSCIsqERGRRFhUiYiIJMKP1JCy2dvLnYCIVIRFlRQtJTxC7ghEpCIc/iUiIpIIiyopmsPiBXBYvEDuGESkEiyqpGjao4ehPXpY7hhEpBIsqkRERBJhUSUiIpIIiyoREZFE+JEaUjRR3lnuCESkIiyqpGip366XOwIRqQiHf4mIiCTCokqKVnb2Zyg7+zO5YxCRSnD4lxTN5vQpuSMQkYrwTJWIiEgiLKpEREQSYVElIiKSCK+pkqIZKr8qdwQiUhEWVVK0Byu+ljsCEakIh3+JiIgkwqJKilY2KBBlgwLljkFEKsHhX1I0mwvn5Y5ARCrCM1UiIiKJsKgSERFJhEWViIhIIrymSoqWU6Om3BGISEVYVEnR0haHyh2BiFSEw79EREQSYVElRXMc/zEcx38sdwwiUgkO/5KiWV/9Q+4IRKQiZj1TTUtLg7e3N27evAkAiI6Oho+PD9q3b48lS5aYs2kiIiKLM1tRPXfuHHr37o24uDgAwKNHjzBlyhQsX74ckZGRuHDhAg4fPmyu5omIiCzObEV106ZNmDFjBipWrAgAiI2NRbVq1fD666/DxsYGPj4+2LNnj7maJyIisjizXVOdM2dOvscJCQnQ6XTGxxUrVkR8fHyx11uhguNzZ7Mknc5J7ghmpdWavgsVZ97ClKgv33m75Mua0YuWR07si8fYD/8ozX1hsRuVhBAFpmk0mmKvJykpDQZDwXW9iHQ6JyQmPpA7htnodE7Q67NNmlertTF53qcpUV9OnfW/hV+c7aD0/aI42BePsR/+Udy+sLLSvFAnWxb7SE2lSpVw9+5d4+OEhATj0DAREZESWKyoNm7cGNeuXcNff/2FnJwc7Nq1C61bt7ZU86RSTiOGwGnEELljEJFKWGz4187ODvPnz8fo0aORmZkJDw8PdOzY0VLNk0pZ3bktdwQiUhGzF9WoqCjjv11dXbFjxw5zN0lERCQLfk0hERGRRFhUiYiIJMLv/iVFy27WXO4IRKQiLKqkaA+DPpM7AhGpCId/iYiIJMKiSor20sD38dLA9+WOQUQqweFfUjTNvWS5IxCRivBMlYiISCIsqkRERBJhUSUiIpIIr6mSoulbecgdgYhUhEWVFC19fKDcEYhIRTj8S0REJBEWVVK0cr38UK6Xn9wxiEglOPxLyvbokdwJiEhFeKZKREQkERZVIiIiibCoEhERSYTXVEnRstp1lDsCEakIiyopWsbIj+WOQEQqwuFfIiIiibCokqKV69oZ5bp2ljsGEakEiyoREZFEWFSJiIgkwqJKREQkERZVIiIiifAjNaRomV26yR2BiFSERZUU7dGgD+WOQEQqwuFfUrb09Mf/ERFZAM9USdHK9QkAAKRsi5Q5CRGpAc9UiYiIJMKiSkREJBEWVSIiIomwqBIREUmENyqRoj3q1VfuCESkIiyqpGiZLKpEZEEc/iVF0yQlQZOUJHcMIlIJnqmSor00uB8Afk6ViCyDZ6pEREQSYVElIiKSCIsqERGRRFhUiYiIJMIblUjRHg0YLHcEIlIRFlVStMyu/nJHICIV4fAvKZrVrZuwunVT7hhEpBI8UyVFcxo5FAA/p0pElsEzVSIiIonIUlS3b98OLy8veHl5YcGCBXJEICIikpzFi2pGRgbmzJmDdevWYfv27Th9+jSio6MtHYOIiEhyFi+qOTk5MBgMyMjIQHZ2NrKzs2FnZ2fpGERERJKz+I1Kjo6OGDNmDDp16gR7e3s0b94cTZs2NXn5ChUczZhOejqdk9wRzEqrNX0XKs68T9JnG0rWl5MmAijedtBnG6C1Me/7TaXvF8XBvniM/fCP0twXFi+qly5dwpYtW3Do0CE4OTnh008/xZo1azBkyBCTlk9KSoPBIMycUho6nRMSEx/IHcNsdDon6PXZJs2r1dqYPG+hy9tYYULI4RIs+dLj/xVj2YVjPMy63ZS+XxQH++Ix9sM/itsXVlaaF+pky+LDv8eOHYOrqysqVKgAW1tb+Pn54dSpU5aOQSqhi78OXfx1uWMQkUpYvKjWrVsX0dHRSE9PhxACUVFRaNiwoaVjkEr4b1oM/02L5Y5BRCph8eHfli1b4rfffoOfnx+0Wi0aNmyIoUOHWjoGERGR5GT5RqWhQ4eykBIRkeLwG5WIiIgkwqJKREQkEX6hPinawfb95I5ARCrCokqKdqVOM7kjEJGKcPiXFO3Vm1fw6s0rcscgIpVgUSVF67J1KbpsXSp3DCJSCRZVIiIiibCoEhERSYRFlYiISCIsqkRERBLhR2pI0XZ7fyh3BCJSERZVUrS/qr8pdwQiUhEO/5KiVbt2AdWuXZA7BhGpBIsqKVqnXavRaddquWMQkUqYVFSnTJlSYNro0aMlD0NERFSaFXlNdcaMGYiPj8eZM2eQnJxsnJ6dnY0///zT7OGIiIhKkyKLakBAAK5cuYLLly+jQ4cOxunW1tZo0qSJ2cMRERGVJkUW1YYNG6Jhw4Zwc3PDK6+8YqlMREREpZJJH6m5fv06JkyYgJSUFAghjNN37txptmBEUtjRbZTcEYhIRUwqqjNnzoS/vz/q168PjUZj7kxEkrn9Wi25IxCRiphUVLVaLQYOHGjuLESSq3X5NAD+WDkRWYZJH6mpVasWLl++bO4sRJJrs28d2uxbJ3cMIlIJk85Ub9y4AX9/f7z66quws7MzTuc1VSIion+YVFTHjRtn7hxERESlnklFtXbt2ubOQUREVOqZVFRbtGgBjUYDIYTx7l+dTocjR46YNRwREVFpYlJRvXTpkvHfer0e+/btyzeN6EW1pcd4uSMQkYoU+1dqtFotvLy8cPz4cXPkIZJUYqWqSKxUVe4YRKQSJp2p3r9/3/hvIQQuXLiA1NRUc2Uikky9C9EAgItvusmchIjUoNjXVAGgQoUKmDp1qlmDEUnB49BGACyqRGQZxb6mSkRERIUzqagaDAasWbMGR44cQXZ2Ntzd3TF8+HDY2Ji0OBERkSqYdKPS4sWLceLECXzwwQcYOHAgzp49i+DgYHNnIyIiKlVMOtU8evQotmzZAq1WCwB499130aVLF0yZMsWs4YiIiEoTk4qqEMJYUAHA1tY232OiF9UP7/OGOiKyHJOKat26dTF37ly8//77AID169fzqwupVEgpX1HuCESkIiZdU50xYwZSU1PRq1cv9OjRA/fu3cO0adPMnY3ouTX+OQqNf46SOwYRqUSRRTUrKwuBgYE4ceIE5s+fj+joaDRq1AjW1tZwdHS0VEaiEnM9vh2ux7fLHYOIVKLIohoaGoq0tDQ0adLEOG3WrFlITU1FWFiY2cMRERGVJkUW1Z9++gmLFy9GhQoVjNMqVaqE4OBgHDhwwOzhiIiISpMii6pWq4W9vX2B6Y6OjrC1tTVbKCIiotKoyKJqZWWFtLS0AtPT0tKQnZ1ttlBERESlUZFF1dvbG0FBQUhPTzdOS09PR1BQENq3b2/2cETP67uBn+O7gZ/LHYOIVKLIovrBBx/AyckJ7u7u6NGjBwICAuDu7o6XXnoJI0eOtFRGohJLd3wZ6Y4vyx2DiFSiyC9/sLKywqxZszBs2DD89ttvsLKyQsOGDVGpUiVL5SN6Ls1O7gYAnHbpJHMSIlIDk75R6bXXXsNrr71m7ixEkmt2ag8AFlUisgyTvlGJiIiInk2WohoVFQU/Pz907NgRs2fPliMCERGR5CxeVG/cuIEZM2Zg+fLl2LlzJ3777TccPnzY0jGIiIgkZ9I1VSnt378fnTt3xiuvvAIAWLJkCezs7Cwdg4iISHIWL6p//fUXtFotBg8ejMTERHh6emLs2LEmL1+hQun6In+dzknuCGal1Zq+CxVnXqmW/27U4mIvq882mH275V2/PtsArY16b29Q+jFiKvbDP0pzX1i8qObk5OD06dNYt24dHBwc8NFHH2Hr1q3w8/MzafmkpDQYDMLMKaWh0zkhMfGB3DHMRqdzgl5v2jdrabU2Js/7NCVZXq/53y5ejGW1NlaYEGK+SxJP9sXCMR6K3k+KovRjxFTsh38Uty+srDQv1MmWxd8e/+tf/4KrqyucnZ1hb2+PNm3aIDY21tIxSCVcj22D67FtcscgIpWweFH19PTEsWPHkJqaipycHBw9ehQNGjSwdAxSicZnD6Hx2UNyxyAilbD48G/jxo0xZMgQ9OnTB3q9Hu7u7vD397d0DCIiIslZvKgCQEBAAAICAuRomoiIyGzUe8shERGRxFhUiYiIJCLL8C+RpXw1OkTuCESkIjxTJSIikgiLKimaR1Q4PKLC5Y5BRCrBokqKVu/XGNT7NUbuGESkEiyqREREEmFRJSIikgiLKhERkUT4kRpSNL2Wv9VLRJbDokqKtmZ4sNwRiEhFOPxLREQkERZVUrS2e79D273fyR2DiFSCRZUUrebvZ1Dz9zNyxyAilWBRJSIikgiLKhERkURYVImIiCTCj9SQoqWXLSd3BCJSERZVUrTvBs2UOwIRqQiHf4mIiCTCokqK1mnnKnTauUruGESkEhz+JUWrFver3BGISEVYVIlkps82QKdzskhbmVnZSE3JsEhbRGrEokokM62NFSaEHLZIWwvHeFikHSK14jVVIiIiifBMlRQt5WWd3BGISEVYVEnRfugXJHcEIlIRDv8SERFJhEWVFK1LRBi6RITJHYOIVILDv6Ror976Q+4IRKQiPFMlIiKSCIsqERGRRFhUiYiIJMJrqqRoiRVflzsCEakIiyop2paen8odgYhUhMO/REREEmFRJUXz37gI/hsXyR2DiFSCw7+kaLqEG3JHICIV4ZkqERGRRFhUiYiIJMKiSkREJBFeUyVFu12lptwRiEhFWFRJ0Xb4jZY7AhGpCId/iYiIJMKiSorWe91s9F43W+4YRKQSHP4lRSt3P1HuCESkIrKdqS5YsACTJk2Sq3kiIiLJyVJUY2JisHXrVjmaJiIiMhuLF9X79+9jyZIlGD58uKWbJiIiMiuLX1OdPn06xo0bhzt37li6aVKhv/7dQO4IRKQiFi2qP/74IypXrgxXV1dERESUaB0VKjhKnMq8Xi5fFlobywwI6LNzoLWxtkhbubRa03eh4swr1fIH/D56vKwF2nqe9Zu7vbx0OieLtWWKFy2PXNgP/yjNfWHRohoZGYnExET4+voiJSUF6enpmDt3LqZMmWLyOpKS0mAwCDOmlI5O5wStjRUmhBy2SHsLx3hYrK3c9vT6bJPm1WptTJ73aZ53+RelrcL6wpLPLTHxgcXaehadzumFyiMX9sM/itsXVlaaF+pky6JF9dtvvzX+OyIiAqdOnSpWQSUqrv7fTAcAfDdopsxJiEgN+DlVUjSHhylyRyAiFZGtqPr5+cHPz0+u5omIiCTHrykkIiKSCIsqERGRRHhNlRTtj9pvyx2BiFSERZUU7UCH/nJHICIV4fAvERGRRFhUSdEGfzURg7+aKHcMIlIJDv+Somn1mXJHICIV4ZkqERGRRFhUiYiIJMKiSkREJBFeUyVFu9jAVe4IRKQiLKqkaIff6yV3BCJSEQ7/EhERSYRFlRRteNgYDA8bI3cMIlIJFlUiIiKJsKgSERFJhEWViIhIIiyqREREEuFHakjRzjXxlDsCEakIiyopWkzLrnJHICIV4fAvKZo26xG0WY/kjkFEKsGiSoo2eGUgBq8MlDsGEakEiyoREZFEWFSJiIgkwqJKREQkERZVIiIiifAjNaRop5t3lDsCEakIiyop2mmXTnJHICIV4fAvKZpD2n04pN2XOwYRqQSLKila/29noP+3M+SOQUQqwaJKREQkERZVIiIiibCoEhERSYRFlYiISCL8SA0pWoy7r9wRiEhFWFRJ0c41fU/uCESkIhz+JUUrdy8B5e4lyB2DiFSCRZUUrff6Oei9fo7cMYhIJVhUiYiIJMKiSkREJBEWVSIiIomwqBIREUmEH6khRTvs2VPuCESkIiyqpGgX33STOwIRqQiHf0nRdPHXoYu/LncMIlIJFlVSNP9Ni+G/abHcMYhIJVhUiYiIJCLLNdWlS5di9+7dAAAPDw9MnDhRjhhERESSsviZanR0NI4dO4atW7di27Zt+PXXX7F//35LxyAiIpKcxc9UdTodJk2aBFtbWwBAjRo1cPv2bUvHICIikpzFi2qtWrWM/46Li0NkZCTCw8NNXr5CBcfnzqDPNkBrY7mTdK3Wct1sybaK297zZivJ8j91HlCiZc3dj0+u31LbTZ9tgE7nZLG2TDnOLJXnRcd++Edp7gvZPqd65coVDBs2DIGBgfj3v/9t8nJJSWkwGMRzta3TOWFCyOHnWocptFobzP3IHXp9ttnbymXJtorTnlZr89zZSrL8pRpN/rdw8ZY1Zz8W1heW2m5aGyuL7PsAsHCMBxITHxQ5j07n9Mx51ID98I/i9oWVlUaSky2pyHL375kzZzBgwACMHz8e3bp1kyMCqcSrN6/g1ZtX5I5BRCph8TPVO3fuYOTIkViyZAlcXV0t3TypTJetSwEAX40OkTkJEamBxYvqmjVrkJmZifnz5xun9erVC71797Z0FCIiIklZvKgGBQUhKCjI0s0SERGZHb9RiYiISCIsqkRERBLhT7+Rou32/lDuCESkIiyqpGh/VX9T7ghEpCIc/iVFq3btAqpduyB3DCJSCRZVUrROu1aj067VcscgIpVgUSUiIpIIiyoREZFEWFSJiIgkwqJKREQkEX6khhRtR7dRckcgIhVhUSVFu/1aLbkjEJGKcPiXFK3W5dOodfm03DGISCV4pkqK1mbfOgDAlTrNZE5CRGrAM1UiIiKJsKgSERFJhEWViIhIIiyqREREEuGNSqRoW3qMlzsCEakIiyopWmKlqnJHICIV4fAvKVq9C9GodyFa7hhEpBI8UyVF8zi0EQBw8U03mZMQkRqwqBIRFdNL5crAzlbal0+dzqnQ6frsHGhtrCVt62ks2RYAZGZlIzUlw2LtWQKLKhFRMdnZ2mBCyGHJ1qfV2kCvzy70bwvHeEjaVlEs2VZue0rDa6pEREQSYVElIiKSCId/SdF+eH+q3BGISEVYVEnRUspXlDsCEakIh39J0Rr/HIXGP0fJHYOIVIJnqqRorse3AwDONX1P5iREpAY8UyUiIpIIiyoREZFEWFSJiIgkwqJKREQkEd6oRIr23cDP5Y5ARCrCokqKlu74stwRiEhFOPxLitbs5G40O7lb7hhEpBIsqqRozU7tQbNTe+SOQUQqwaJKREQkERZVIiIiibCoEhERSYRFlYiISCL8SA0p2pphC+SOQEQqwqJKiqa3tZc7AhGpCId/SdFcj22D67FtcscgIpVgUSVFa3z2EBqfPSR3DCJSCRZVIiIiichSVHfu3InOnTujXbt22LBhgxwRiIiIJGfxG5Xi4+OxZMkSREREwNbWFr169YKLiwtq1qxp6ShERESSsnhRjY6ORosWLfDyyy8DADp06IA9e/Zg1KhRJi1vZaWRJEd5JztJ1lMUG62NxdrKZcm2itOejdYG2Xpri7SVr90qr5ZoWXP2Y2F9odR9xJTjVapj2tKk7MdnHR9K3T+Awrd/cfaJF23/0QghhCUbXLlyJdLT0zFu3DgAwI8//ojY2FjMmjXLkjGIiIgkZ/FrqoXVcI3mxXqnQUREVBIWL6qVKlXC3bt3jY8TEhJQsWJFS8cgIiKSnMWLqpubG2JiYpCcnIyMjAzs27cPrVu3tnQMIiIiyVn8RqVKlSph3Lhx6N+/P/R6PQICAtCoUSNLxyAiIpKcxW9UIiIiUip+oxIREZFEWFSJiIgkwqJKREQkERZVIiIiifBHyiW0dOlS7N69GwDg4eGBiRMnYvLkyThz5gzKlCkDABg1ahTatWsnZ0yLCAkJwd69e6HRaBAQEICBAwciOjoa8+bNQ2ZmJjp16mT8Vi0lK6wf1LpP5FqwYAHu3buH+fPn4+LFiwgKCkJaWhqaNWuGzz//HDY26nlZytsXS5cuxZYtW/DSSy8BAHr06IG+ffvKnND8+vfvj6SkJON2nzlzJq5fv44VK1ZAr9djwIABpasfBEni+PHjomfPniIzM1NkZWWJ/v37i3379glvb28RHx8vdzyLOnnypOjVq5fQ6/UiIyNDeHp6iosXLwoPDw9x/fp1odfrxaBBg8RPP/0kd1SzKqwfrl69qsp9Ild0dLRwcXERgYGBQgghvLy8xNmzZ4UQQkyePFls2LBBxnSW9WRfDBs2TPz8888yp7Isg8Eg3N3dhV6vN077+++/haenp7h37554+PCh8PHxEVeuXJExZfFw+FciOp0OkyZNgq2tLbRaLWrUqIHbt2/j9u3bmDZtGnx8fBAaGgqDwSB3VLNr3rw5vvvuO9jY2CApKQk5OTlITU1FtWrV8Prrr8PGxgY+Pj7Ys2eP3FHNqrB+sLOzU+U+AQD379/HkiVLMHz4cADArVu38OjRI7z11lsAAD8/P8XvE7me7AsAuHDhAlavXg0fHx/MnDkTmZmZMia0jD///BMajQYffvghunTpgvXr1+f70RUHBwfjj66UFiyqEqlVq5bxxSEuLg6RkZFo1aoVWrRogblz52LTpk04ffo0Nm/eLG9QC9FqtQgNDYWXlxdcXV2RkJAAnU5n/HvFihURHx8vY0LLeLIfcnJyVLtPTJ8+HePGjTMObz65T+h0OlXsE0DBvnj48CHq1auHwMBAbN26FampqVi+fLnMKc0vNTUVrq6uWLZsGf7v//4P4eHhuH37dql+rWBRldiVK1cwaNAgBAYG4o033sCyZctQoUIFlClTBv369cPhw4fljmgxH3/8MWJiYnDnzh3ExcUV+Ltafkghbz/ExMSocp/48ccfUblyZbi6uhqnCZX+uEZhfVG2bFmsXr0a1apVg42NDQYNGqSK/aJJkyYIDg6Gg4MDnJ2dERAQgNDQ0ALzlab9Qj13BFjAmTNn8PHHH2PKlCnw8vLC5cuXERcXhw4dOgB4/CKihpswrl69iqysLNSrVw9lypRB+/btsWfPHlhb//N7kWr4IYXC+iEyMhIvv/yy6vaJyMhIJCYmwtfXFykpKUhPT4dGo8n34xqJiYmK3yeAwvti8uTJePvttxEQEABAPfvF6dOnodfrjW8whBCoUqVK6f7RFVmv6CrI7du3hYuLi4iOjjZOu3jxomjdurW4f/++yMrKEoMGDRI7d+6UMaVl/PTTT8LPz09kZmaKzMxMMXDgQLFr1y7RunVrERcXJ7Kzs8XgwYNFZGSk3FHNqrB+CAsLU+U+kdeWLVvy3ah0+vRpIYQQU6dOFatXr5YzmsXl9kVSUpJo3ry5uH79ujAYDGLy5Mniq6++kjue2UVFRYmuXbuKR48eiQcPHggfHx9x+vRp4enpKZKSkkR6erro0qWLOHfunNxRTab8t0IWsmbNGmRmZmL+/PnGab169cLQoUPRu3dvZGdno3379vD29pYxpWV4eHjg3Llz6Nq1K6ytrdG+fXt4eXnB2dkZo0ePRmZmJjw8PNCxY0e5o5pVYf0watQolC9fXnX7xNMsWrQIQUFBePjwIerXr4/+/fvLHUkWzs7OmDlzJkaMGAG9Xo+mTZti4MCBcscyO09PT+MxYjAY0KdPH7z99tul+kdX+IX6REREEuGNSkRERBJhUSUiIpIIiyoREZFEWFSJiIgkwqJKREQkERZVomKoU6cOkpOT802LiIjAsGHDjI+Tk5MRFBSEdu3aoUuXLujatSvWrFmDnJwcAMDNmzdRp06dQn95Y/LkyYW2IYWlS5fiwIEDkq0vJycHI0aMQIcOHbB+/XrJ1ktUmrGoEkkoNTUVvXv3xr///W/s3r0bO3bswNq1axEbG4sJEyYY57Ozs0NcXBxu3bplnJaeno4zZ86YLdvJkyeRnZ0t2fri4+Nx7NgxREZG4v3335dsvUSlGb/8gUhCP/zwA+rVq4chQ4YYp5UrVw7BwcHw9PREbGwsnJ2dYW1tjU6dOmHnzp3GXyrZt28f2rRpg2+++eaZ7UyaNAkajQZXr15FcnIy3N3dERQUBK1Wi3PnzmH27NnIyMiAVqvFxIkT8eeff+LChQsIDg6GtbU1Dh48+NTl33zzTbRp0waXLl3CokWLkJmZieDgYOP6xo4di6ZNm2LIkCHIzs6Gn58fwsLCULVqVbP1K1FpwTNVomL64IMP4Ovra/wv7xeAnz17Fu+8806BZezs7PD222/j559/Nk7r2rUrduzYYXy8bds2dOvWzeQcly5dwrfffovIyEhcvXoVGzduhF6vx8iRIzFy5Ejs2rULs2bNwty5c9G7d2+8+eabmDhxovEH0QtbHgD0ej08PT2xd+9evPbaa/j4448xdepU7Ny5EwsWLMCECRNw7949rFq1Cvb29ti+fTsLKtH/8EyVqJjWrl0LZ2dn4+OIiAjs3bvX+Fiv1xe6XFZWVr7Hb775JqysrHDhwgVUqFABDx8+RO3atU3O0a1bN5QtWxYA4Ovri4MHD6JJkyawsrLCu+++a2xj586dJi+fO4zbrFkzAEBsbCyqVq2Kxo0bA3j8E4dNmzbFqVOn4OLiYnJWIrXgmSqRhHILzpMePnyI8+fPo2nTpvmmd+nSBTt27MD27dvh6+tbrLby/uqPEAJWVlawtrYu8DNZv//+e6HXUgtbPpeDgwMAFPoD6kIISa/NEikJiyqRhPr06YOrV69i1apVxrt9U1JSMGnSJDRr1qzAF4P7+vpiz549iIyMLPYX6+/evRtZWVnIzMzE1q1b4enpiTfeeAMajQbHjx8HAPz666/44IMPYDAYYG1tna8YFrb8kxo3boxr164hNjYWwOPfC/7vf/+L5s2bFysrkVpw+JdIQo6Ojti4cSNCQkLQuXNnaLVaaDQa+Pj4YNCgQQXmr1SpEmrUqAEnJye8/PLLxWrL3t4effr0QWpqKjp06AB/f39YWVkhLCwMc+fORXBwMLRaLcLCwmBrawtPT08sWLDAODxd2PJPcnZ2RkhICGbNmoVHjx5Bo9Fg3rx5qF69Om7evFmiPiJSMv5KDVEpNGnSJNSqVQuDBw+WZXkiKhzPVIleQH/++SfGjRtX6N+qV68Oe3t7CyciIlPwTJWIiEgivFGJiIhIIiyqREREEmFRJSIikgiLKhERkURYVImIiCTCokpERCSR/wdvZtx09LubBgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = mean_state_l, x = mean_state_l['HOM_pctprof'], bins=13)\n",
    "plt.axvline(x=mean_state_l['HOM_pctprof'].mean(),color='red', ls='--')\n",
    "plt.title(\"Histogram of the Percent of Homeless Students who were Proficient in RLA by State\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike the two previous data sets, the RLA's homeless population's distribution does not seem very normal. This is due to the outliers that we elected to keep in the section above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis and Results "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choropleth - The choropleths show the difference of non-homeless statistics to homeless statistics for all three data sets. So in the graduation data set for example, it shows (Non-homeless graduation rate) - (Homeless graduation rate) for each state. \n",
    "\n",
    "Two sample Kolmogorow Smirnov Test - It does not assume any specific distribution. It tests whether two samples comes from the same population. Specifically, it is based on the maximum distance between the empirical distribution function of both samples. \n",
    "\n",
    "T-test - Parametric test which sees if the means of two populations are significantly different. \n",
    "\n",
    "For both tests, we use an alpha of 0.05. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Homeless to Non-Homeless Students' Graduation Rates "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choropleth "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(18, 18))\n",
    "grad_gdf.plot(\n",
    "    column='diff', \n",
    "    ax=ax, \n",
    "    legend=True, \n",
    "    legend_kwds={\n",
    "        'shrink': 0.6,\n",
    "        'label': 'Diff in Grad Rate by State'\n",
    "    },     \n",
    "    missing_kwds={\n",
    "        \"color\": \"lightgrey\",\n",
    "        \"label\": \"Missing values\",\n",
    "    },\n",
    "    cmap = \"OrRd\")\n",
    "plt.xlim([-130, -65])\n",
    "plt.ylim([20,70])\n",
    "plt.title('Diff in Grad Rates of Non-Homeless and Homeless students', fontsize=19)\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to reiterate. This is the difference from Non-homeless students' graduation rates to that of Homeless students. So looking at the choropleth, we can see that non-homeless students had much higher (~60) graduation rates than homeless students. Whereas states like Louisiana had very little difference in their graduation rates. \n",
    "\n",
    "Overall, there isn't much to say about regional differences in graduation rates between homeless and non-homeless students. The general trend is that non-homeless students have higher graduation rates than non-homeless students, but by what degree varies pretty randomly across the US. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Two Sample Kolmogorov Smirnov Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.8333333333333334, pvalue=9.236090683675521e-11)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the total column because it's not a state\n",
    "mean_gradRates = mean_gradRates.drop(index=\"Total\")\n",
    "\n",
    "#Perform the test \n",
    "hom_grad = mean_gradRates[['HOM_rate']].squeeze().sample(n=30, random_state=1)\n",
    "nonHom_grad = mean_gradRates[['NOTHOM_rate']].squeeze().sample(n=30, random_state=1)\n",
    "ks_2samp(hom_grad, nonHom_grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our critical value $D_\\alpha = c(\\alpha)\\sqrt{\\frac{n_1 + n_2}{n_1 n_2}}$. \n",
    "We used sample sizes of 30 and with a $\\alpha = .05$, $c(\\alpha) = 1.36$.\n",
    "\n",
    "When plugged into our formula, we get a $D_\\alpha \\approx .35115$. Our statistic was 0.833. Since 0.833 > .35115, we reject the null hypothesis and find that the samples are not drawn from the same distribution. Just as a note, our statistic was still greater than $D_\\alpha$ when we calculated $D_\\alpha$ using an alpha of 0.001 (0.503), so we can be very certain that these two samples do not come from the same distribution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One-tailed Two Sample T test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The populations were somewhat normally distributed and had enough observations to statisfy the Central Limit Theorem, so we thought it was okay to use a t-test to compare the homeless and non-homeless populations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by ensuring that the variances of the two are the same by using the 4:1 ratio general practice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkVar = False \n",
    "# Find which is larger and then ensure the 4:1 ratio is not exceeded\n",
    "if (hom_grad.var() > nonHom_grad.var()): \n",
    "    checkVar = (hom_grad.var() / nonHom_grad.var())  < 4\n",
    "else: \n",
    "    checkVar = (nonHom_grad.var() / hom_grad.var()) < 4\n",
    "checkVar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The above code is just an admittedly overengineered way of checking if the variances do not exceed the 4:1 ratio. Since the Boolean value is true, we can assume the variances do not exceed the 4:1 ratio and we can assume the variances are equal for our t test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-11.61952051582352, 8.918394862687397e-17)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(a=hom_grad, b=nonHom_grad)\n",
    "t_val, p_val "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since both samples had 30 observations, the total degrees of freedom was 60-2 = 58. Using the one-tailed critical value t-table at https://www.statisticshowto.com/tables/t-distribution-table/, we find that the critical t value is $\\approx$ -1.671 at an alpha of .05. \n",
    "\n",
    "Our test statistic was -11.61 which is much less than -1.671. This indicates that the homeless graduation rate was significantly lower than that of the non-homeless student population. As a matter of fact, we could run the test with an alpha of .0005 (crit value $\\approx$ -3.460) and still reject the null hypothesis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Homeless to Non-Homeless Students' Percent Proficiencies in Math "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choropleth "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(18, 18))\n",
    "math_gdf.plot(\n",
    "    column='diff', \n",
    "    ax=ax, \n",
    "    legend=True, \n",
    "    legend_kwds={\n",
    "        'shrink': 0.6,\n",
    "        'label': 'Diff in Math % Proficiency by State'\n",
    "    },     \n",
    "    missing_kwds={\n",
    "        \"color\": \"lightgrey\",\n",
    "        \"label\": \"Missing values\",\n",
    "    },\n",
    "    cmap = \"PuBu\")\n",
    "plt.xlim([-130, -65])\n",
    "plt.ylim([15,70])\n",
    "plt.title('Diff in Math Percent Proficiency of Non-Homeless and Homeless students', fontsize=19)\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the choropleth shows the difference of non-homeless students' percent proficiency in math compared to that of homeless students. Note that in this choropleth, there are negative values which indicates that in those states, homeless students did better than non-homeless students on average. Wyoming for example had a homeless population that outperformed their non-homeless population by about 10%. \n",
    "\n",
    "Overall, there really is no regional trend in the US states / territories in the difference in academic performance of non-homeless to homeless students in math. Generally speaking, the non-homeless population outperforms the homeless population but there's a wide variety in differences in performance across the US. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Two Sample Kolmogorov Smirnov Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.4, pvalue=0.015643390496754332)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop total because it's not a state\n",
    "mean_state_m = mean_state_m.drop(index=\"Total\")\n",
    "\n",
    "# Perform the test\n",
    "hom_math = mean_state_m[['HOM_pctprof']].squeeze().sample(n=30, random_state=1)\n",
    "nonHom_math = mean_state_m[['NOTHOM_pctprof']].squeeze().sample(n=30, random_state=1)\n",
    "ks_2samp(hom_math, nonHom_math)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our critical value $D_\\alpha = c(\\alpha)\\sqrt{\\frac{n_1 + n_2}{n_1 n_2}}$. \n",
    "We used sample sizes of 30 and with a $\\alpha = .05$, $c(\\alpha) = 1.36$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When plugged into our formula, we get a $D_\\alpha \\approx .35115$. The test statistic was 0.4 so we can reject the null hypothesis, which means that our samples were not drawn from the same distribution (The homeless and non-homeless populations' proficiency percentages were statistically different). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One-tailed Two Sample T test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The populations were somewhat normally distributed and had enough observations to statisfy the Central Limit Theorem so we thought it was ok to use a t-test to compare the homeless and non-homeless populations.\n",
    "\n",
    "We start by checking if the variances between populations is the same using the 4:1 ratio rule of thumb:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkVar = False \n",
    "# Find which is larger and then ensure the 4:1 ratio is not exceeded\n",
    "if (hom_math.var() > nonHom_math.var()): \n",
    "    checkVar = (hom_math.var() / nonHom_math.var())  < 4\n",
    "else: \n",
    "    checkVar = (nonHom_math.var() / hom_math.var()) < 4\n",
    "checkVar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The above code is just an admittedly overengineered way of checking if the variances do not exceed the 4:1 ratio. Since the Boolean value is true, we can assume the variances do not exceed the 4:1 ratio and we can assume the variances are equal for our t test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.1567029296089784, 0.035186348431638106)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(a=hom_math, b=nonHom_math)\n",
    "t_val, p_val "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since both samples had 30 observations, the total degrees of freedom was 60-2 = 58. Using the one-tailed critical value t-table at https://www.statisticshowto.com/tables/t-distribution-table/, we find that the critical t value is $\\approx$ -1.671 at an alpha of .05. \n",
    "\n",
    "Our test statistic was -2.20 which is slightly less than -1.671. This indicates that the percent proficiency of homeless students was significantly lower than that of the non-homeless student population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Homeless to Non-Homeless Students' Percent Proficiencies in RLA "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choropleth "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(18, 18))\n",
    "rla_gdf.plot(\n",
    "    column='diff', \n",
    "    ax=ax, \n",
    "    legend=True, \n",
    "    legend_kwds={\n",
    "        'shrink': 0.6,\n",
    "        'label': 'Diff in RLA % Proficiency by State'\n",
    "    },     \n",
    "    missing_kwds={\n",
    "        \"color\": \"lightgrey\",\n",
    "        \"label\": \"Missing values\",\n",
    "    },\n",
    "    cmap = \"PuBu\")\n",
    "plt.xlim([-130, -65])\n",
    "plt.ylim([15,70])\n",
    "plt.title('Diff in RLA Percent Proficiency of Non-Homeless and Homeless students', fontsize=19)\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the choropleth shows the difference of non-homeless students' percent proficiency in RLA compared to that of homeless students. Once again, there are negative values which indicate that in those states, the homeless population did better than the non-homeless on average. Interestingly, the states where non-homeless students greatly outperformed the homeless students are pretty similar between the math and RLA achievement data but the reverse is not true (i.e. states where homeless students greatly outperformed the non-homeless students are not similar between math and rla achievement data)\n",
    "\n",
    "Unlike the previous two choropleths, there is potentially a small indication that the non-homeless students out perform the homeless students in the northwestern region of the US. But again, it seems weak and the difference in non-homeless to homeless populations seems to vary with no relation to region. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Two Sample Kolmogorov Smirnov Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.5666666666666667, pvalue=8.737803591291227e-05)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the total row because it's not a state \n",
    "mean_state_l = mean_state_l.drop(index=\"Total\") \n",
    "\n",
    "# Perform the test\n",
    "hom_rla = mean_state_l[['HOM_pctprof']].squeeze().sample(n=30, random_state=1)\n",
    "nonHom_rla = mean_state_l[['NOTHOM_pctprof']].squeeze().sample(n=30, random_state=1)\n",
    "ks_2samp(hom_rla, nonHom_rla)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our critical value $D_\\alpha = c(\\alpha)\\sqrt{\\frac{n_1 + n_2}{n_1 n_2}}$. \n",
    "We used sample sizes of 30 and with a $\\alpha = .05$, $c(\\alpha) = 1.36$.\n",
    "\n",
    "When plugged into our formula, we get a $D_\\alpha \\approx .35115$. The test statistic was 0.566 so we can reject the null hypothesis, which means that our samples were not drawn from the same distribution (The homeless and non-homeless populations' proficiency percentages were statistically different). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One-Tailed Two Sample T test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the same reasons listed in the above section, we thought it was reasonable to use a t-test to compare mean scores. \n",
    "\n",
    "Just like in graduation rates and math achievement data, we start by looking at the variances of the two populations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkVar = False \n",
    "# Find which is larger and then ensure the 4:1 ratio is not exceeded\n",
    "if (hom_rla.var() > nonHom_rla.var()): \n",
    "    checkVar = (hom_rla.var() / nonHom_rla.var())  < 4\n",
    "else: \n",
    "    checkVar = (nonHom_rla.var() / hom_rla.var()) < 4\n",
    "checkVar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The above code is just an admittedly overengineered way of checking if the variances do not exceed the 4:1 ratio. Since the Boolean value is true, we can assume the variances do not exceed the 4:1 ratio and we can assume the variances are equal for our t test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
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    {
     "data": {
      "text/plain": [
       "(-3.63908649167899, 0.0005837021221906589)"
      ]
     },
     "execution_count": 61,
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   ],
   "source": [
    "t_val2, p_val2 = ttest_ind(a=hom_rla, b=nonHom_rla)\n",
    "t_val2, p_val2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since both samples had 30 observations, the total degrees of freedom was 60-2 = 58. Using the one-tailed critical value t-table at https://www.statisticshowto.com/tables/t-distribution-table/, we find that the critical t value is $\\approx$ -1.671 at an alpha of .05. \n",
    "\n",
    "Our test statistic was -3.639 which is less than -1.671. This indicates that the percent proficiency of homeless students was significantly lower than that of the non-homeless student population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the information regarding graduation rates and standardized testing results is public. Since we are analyzing groups of people, there is no private or personal information within our data set and, therefore, none that can be traced back to a specific individual and their particular academic achievements. In terms of confidentiality and privacy of particpants, it can be seen that good practice was relied upon. \n",
    "\n",
    "As for income information of students, that information is also made available publicly and no information in our data set could be traced back to a specific individual in a way that infringes on their privacy. Since income information is self-reported there is the danger of self-reporting biased within that data set. However, we take into consideration only information from official sources as to minimize that bias as much as possible. \n",
    "\n",
    "Throughout this project, all information is gathered from official and public sources. Said information is taken from groups of people based on their economic and academic background and none of the information can be traced back to any particular individual as to maintain the privacy of all subjects. \n",
    "\n",
    "We aimed to minimize both biases and privacy issues, however, there can be still be a few related to our data and topic. One of those being, the inequality of data depending on the location(i.e. state) a person was as specific states such as California had an overwhelmingly large amount of data and some states having close to none and having to rely on estimates. To combat against this, we were able to get rid of some data points in our cleaning process and EDA as well to potentially level the playing field but we realize that there may still be some disparities in our data. \n",
    "\n",
    "Apart from this, our research may create negative biases and stereotypes towards homeless people since it shows that their academic performances are not in par with non-homeless students. However, this should be seen as an indicator to create services and programs to establish resources for the less priveleged. Rather than turning negative energy towards them, we have to do our best to support them. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The limitations with our project mostly have to do with the quality and quantity of data available, as well as more general limitations with our topic in general. \n",
    "\n",
    "Addressing the more general issue first, homelessness, by nature, is difficult to report accurately. This is even more true for unsheltered homeless people. [The Committee on Health Care for Homeless People](https://www.ncbi.nlm.nih.gov/books/NBK218229/) outlines common methods used to gain this data and discusses how difficult it is to just get an accurate estimate for a census, much less specific data like the graduation rates of homeless children. All that is to say that even if we are given all the graduation rates for homeless children in schools, these are only addressing those already enrolled in schools—there are potentially several children who are not enrolled in schools that are unaccounted for, making the numbers seem rather low.\n",
    "\n",
    "This leads into the quantity of data available. Most states besides California have very little data available—even for homeless children enrolled in schools—to the point where it’s almost impossible to draw aggregate trends. Additionally, we originally wanted to include children in foster care in our analysis, but there was so little data available we were forced to scrap that section. \n",
    "\n",
    "Finally, out of the data that is available, a lot of its reliability is questionable. For example, there are certain rows in our CSV files where a manual calculation reveals that the total number of graduates in the row is somehow higher than the total number of students in each category, which is impossible. Further, because there is so little data available, a lot of it is obfuscated to protect the privacy of students involved, adding an additional layer of difficulty to the analysis. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have been aware of these limitations the entire time, however, we were able to keep them in mind and account for them, allowing us to still find stastically significant trends and analyse them.\n",
    "\n",
    "By using both parametric and non-parametric tests, we were able to find that on average, across all states, there was a statistically significant trend of homeless students underperforming academically when compared to non-homeless students. This was true across all the factors that we measured—graduation rates and academic achievement across both math and reading/language testing. \n",
    "\n",
    "Of course, there is significant variation from state to state. Wyoming, for example, actually had their non-homeless students underperform compared to their homeless peers by a significant amount. Louisiana had almost the same values for the graduation rates of both student types. This could be due to a multitude of factors, most notably the amount of data available; both these states ranked near the bottom for amount of valid data in almost every case. \n",
    "\n",
    "There are no clear-cut regional trends—each state seems to have their own methods for data collection, their own agencies that deal with homelessness, and so on, leading to a naturally different result in each state. That being said, however, every state did, to some degree, show that homeless students underperformed compared to their non-homeless counterparts academically. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Chris: Coded Data Cleaning, coded EDA, coded Data Analysis and Results, Background\n",
    "- Ana: Data Cleaning, Background & Prior Work, Privacy/Ethics Consideration\n",
    "- Uday: Data Analysis + Results, Limitations, Conclusion\n",
    "- Albert: Background & Prior Work, Hypothesis, Privacy/Ethics Consideration"
   ]
  }
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