Mission217Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy\n",
    "import re\n",
    "\n",
    "data_files = [\n",
    "    \"ap_2010.csv\",\n",
    "    \"class_size.csv\",\n",
    "    \"demographics.csv\",\n",
    "    \"graduation.csv\",\n",
    "    \"hs_directory.csv\",\n",
    "    \"sat_results.csv\"\n",
    "]\n",
    "data = {}\n",
    "\n",
    "for f in data_files:\n",
    "    key_name = f.replace(\".csv\", \"\")\n",
    "    d = pd.read_csv(f\"schools/{f}\")\n",
    "    data[key_name] = d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in the Surveys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_survey = pd.read_csv(\"schools/survey_all.txt\", delimiter=\"\\t\", encoding='windows-1252')\n",
    "d75_survey = pd.read_csv(\"schools/survey_d75.txt\", delimiter=\"\\t\", encoding='windows-1252')\n",
    "survey = pd.concat([all_survey, d75_survey], axis=0)\n",
    "\n",
    "survey = survey.copy()\n",
    "survey[\"DBN\"] = survey[\"dbn\"]\n",
    "\n",
    "survey_fields = [\n",
    "    \"DBN\", \n",
    "    \"rr_s\", \n",
    "    \"rr_t\", \n",
    "    \"rr_p\", \n",
    "    \"N_s\", \n",
    "    \"N_t\", \n",
    "    \"N_p\", \n",
    "    \"saf_p_11\", \n",
    "    \"com_p_11\", \n",
    "    \"eng_p_11\", \n",
    "    \"aca_p_11\", \n",
    "    \"saf_t_11\", \n",
    "    \"com_t_11\", \n",
    "    \"eng_t_11\", \n",
    "    \"aca_t_11\", \n",
    "    \"saf_s_11\", \n",
    "    \"com_s_11\", \n",
    "    \"eng_s_11\", \n",
    "    \"aca_s_11\", \n",
    "    \"saf_tot_11\", \n",
    "    \"com_tot_11\", \n",
    "    \"eng_tot_11\", \n",
    "    \"aca_tot_11\",\n",
    "]\n",
    "survey = survey[survey_fields]\n",
    "data[\"survey\"] = survey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add DBN Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"hs_directory\"][\"DBN\"] = data[\"hs_directory\"][\"dbn\"]\n",
    "\n",
    "def pad_csd(num):\n",
    "    string_representation = str(num)\n",
    "    if len(string_representation) > 1:\n",
    "        return string_representation\n",
    "    else:\n",
    "        return \"0\" + string_representation\n",
    "    \n",
    "data[\"class_size\"][\"padded_csd\"] = data[\"class_size\"][\"CSD\"].apply(pad_csd)\n",
    "data[\"class_size\"][\"DBN\"] = data[\"class_size\"][\"padded_csd\"] + data[\"class_size\"][\"SCHOOL CODE\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert Columns to Numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['SAT Math Avg. Score', 'SAT Critical Reading Avg. Score', 'SAT Writing Avg. Score']\n",
    "for c in cols:\n",
    "    data[\"sat_results\"][c] = pd.to_numeric(data[\"sat_results\"][c], errors=\"coerce\")\n",
    "\n",
    "data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]\n",
    "\n",
    "def find_lat(loc):\n",
    "    coords = re.findall(\"\\(.+, .+\\)\", loc)\n",
    "    lat = coords[0].split(\",\")[0].replace(\"(\", \"\")\n",
    "    return lat\n",
    "\n",
    "def find_lon(loc):\n",
    "    coords = re.findall(\"\\(.+, .+\\)\", loc)\n",
    "    lon = coords[0].split(\",\")[1].replace(\")\", \"\").strip()\n",
    "    return lon\n",
    "\n",
    "data[\"hs_directory\"][\"lat\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lat)\n",
    "data[\"hs_directory\"][\"lon\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lon)\n",
    "\n",
    "data[\"hs_directory\"][\"lat\"] = pd.to_numeric(data[\"hs_directory\"][\"lat\"], errors=\"coerce\")\n",
    "data[\"hs_directory\"][\"lon\"] = pd.to_numeric(data[\"hs_directory\"][\"lon\"], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Condense Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_size = data[\"class_size\"]\n",
    "class_size = class_size[class_size[\"GRADE \"] == \"09-12\"]\n",
    "class_size = class_size[class_size[\"PROGRAM TYPE\"] == \"GEN ED\"]\n",
    "\n",
    "class_size = class_size.groupby(\"DBN\").agg('mean', numeric_only=True)\n",
    "class_size.reset_index(inplace=True)\n",
    "data[\"class_size\"] = class_size\n",
    "\n",
    "data[\"demographics\"] = data[\"demographics\"][data[\"demographics\"][\"schoolyear\"] == 20112012]\n",
    "\n",
    "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Cohort\"] == \"2006\"]\n",
    "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Demographic\"] == \"Total Cohort\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert AP Scores to Numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['AP Test Takers ', 'Total Exams Taken', 'Number of Exams with scores 3 4 or 5']\n",
    "\n",
    "for col in cols:\n",
    "    data[\"ap_2010\"][col] = pd.to_numeric(data[\"ap_2010\"][col], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combine the Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = data[\"sat_results\"]\n",
    "\n",
    "combined = combined.merge(data[\"ap_2010\"], on=\"DBN\", how=\"left\")\n",
    "combined = combined.merge(data[\"graduation\"], on=\"DBN\", how=\"left\")\n",
    "\n",
    "to_merge = [\"class_size\", \"demographics\", \"survey\", \"hs_directory\"]\n",
    "\n",
    "for m in to_merge:\n",
    "    combined = combined.merge(data[m], on=\"DBN\", how=\"inner\")\n",
    "\n",
    "combined = combined.fillna(combined.mean(numeric_only=True))\n",
    "combined = combined.infer_objects(copy=False).fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add a School District Column for Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_first_two_chars(dbn):\n",
    "    return dbn[0:2]\n",
    "\n",
    "combined = combined.copy()\n",
    "combined[\"school_dist\"] = combined[\"DBN\"].apply(get_first_two_chars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAT Critical Reading Avg. Score    0.986820\n",
      "SAT Math Avg. Score                0.972643\n",
      "SAT Writing Avg. Score             0.987771\n",
      "sat_score                          1.000000\n",
      "AP Test Takers                     0.523140\n",
      "                                     ...   \n",
      "priority08                              NaN\n",
      "priority09                              NaN\n",
      "priority10                              NaN\n",
      "lat                               -0.121029\n",
      "lon                               -0.132222\n",
      "Name: sat_score, Length: 67, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "correlations = combined.corr(numeric_only=True)\n",
    "correlations = correlations[\"sat_score\"]\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Survey Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Remove DBN since it's a unique identifier, not a useful numerical value for correlation.\n",
    "survey_fields.remove(\"DBN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdf6c66760>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "combined.corr(numeric_only=True)[\"sat_score\"][survey_fields].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are high correlations between `N_s`, `N_t`, `N_p`, and `sat_score`. Since these columns are correlated with `total_enrollment`, it makes sense that they would be high.  \n",
    "\n",
    "It is more interesting that `rr_s`, the student response rate, or the percentage of students that completed the survey, correlates with `sat_score`. This might make sense because students who are more likely to fill out surveys may be more likely to also be doing well academically.\n",
    "\n",
    "How students and teachers percieved safety (`saf_t_11` and `saf_s_11`) correlate with `sat_score`. This make sense — it's difficult to teach or learn in an unsafe environment.\n",
    "\n",
    "The last interesting correlation is the `aca_s_11`, which indicates how the student perceives academic standards, correlates with `sat_score`, but this is not true for `aca_t_11`, how teachers perceive academic standards, or `aca_p_11`, how parents perceive academic standards."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Safety"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdf5e63ac0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"saf_s_11\", \"sat_score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There appears to be a correlation between SAT scores and safety, although it isn't very strong. It looks like there are a few schools with extremely high SAT scores and high safety scores. There are a few schools with low safety scores and low SAT scores. No school with a safety score lower than `6.5` has an average SAT score higher than 1500 or so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Borough Safety"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "boro\n",
      "Bronx            6.606577\n",
      "Brooklyn         6.370755\n",
      "Manhattan        6.831370\n",
      "Queens           6.721875\n",
      "Staten Island    6.530000\n",
      "Name: saf_s_11, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "boros = combined.groupby(\"boro\").agg('mean', numeric_only=True)[\"saf_s_11\"]\n",
    "print(boros)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like Manhattan and Queens tend to have higher safety scores, whereas Brooklyn has low safety scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Racial Differences in SAT Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdf741a9d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "race_fields = [\"white_per\", \"asian_per\", \"black_per\", \"hispanic_per\"]\n",
    "combined.corr(numeric_only=True)[\"sat_score\"][race_fields].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like a higher percentage of white or Asian students at a school correlates positively with SAT scores, whereas a higher percentage of black or Hispanic students correlates negatively with SAT score. This may be due to a lack of funding for schools in certain areas, which are more likely to have a higher percentage of black or Hispanic students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdf6565eb0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"hispanic_per\", \"sat_score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44                         MANHATTAN BRIDGES HIGH SCHOOL\n",
      "82      WASHINGTON HEIGHTS EXPEDITIONARY LEARNING SCHOOL\n",
      "89     GREGORIO LUPERON HIGH SCHOOL FOR SCIENCE AND M...\n",
      "125                  ACADEMY FOR LANGUAGE AND TECHNOLOGY\n",
      "141                INTERNATIONAL SCHOOL FOR LIBERAL ARTS\n",
      "176     PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE\n",
      "253                            MULTICULTURAL HIGH SCHOOL\n",
      "286               PAN AMERICAN INTERNATIONAL HIGH SCHOOL\n",
      "Name: SCHOOL NAME, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(combined[combined[\"hispanic_per\"] > 95][\"SCHOOL NAME\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The schools listed above appear to primarily serve recent immigrants to the U.S. These schools have many students who are learning English, which would explain the lower SAT scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37                                STUYVESANT HIGH SCHOOL\n",
      "151                         BRONX HIGH SCHOOL OF SCIENCE\n",
      "187                       BROOKLYN TECHNICAL HIGH SCHOOL\n",
      "327    QUEENS HIGH SCHOOL FOR THE SCIENCES AT YORK CO...\n",
      "356                  STATEN ISLAND TECHNICAL HIGH SCHOOL\n",
      "Name: SCHOOL NAME, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(combined[(combined[\"hispanic_per\"] < 10) & (combined[\"sat_score\"] > 1800)][\"SCHOOL NAME\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of the schools above appear to be specialized science and technology schools that receive extra funding and only admit students who pass an entrance exam. This doesn't explain the low `hispanic_per`, but it does explain why their students tend to do better on the SAT — they are students from all over New York City who did well on a standardized test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gender Differences in SAT Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdf649a610>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_fields = [\"male_per\", \"female_per\"]\n",
    "combined.corr(numeric_only=True)[\"sat_score\"][gender_fields].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot above, we can see that a high percentage of females at a school positively correlates with SAT scores, whereas a high percentage of males at a school negatively correlates with SAT scores. Neither correlation is extremely strong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdf649a3a0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"female_per\", \"sat_score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the scatter plot, there doesn't seem to be any real correlation between `sat_score` and `female_per`.  However, there is a cluster of schools with a high percentage of females (`60` to `80`) and high SAT scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5                         BARD HIGH SCHOOL EARLY COLLEGE\n",
      "26                         ELEANOR ROOSEVELT HIGH SCHOOL\n",
      "60                                    BEACON HIGH SCHOOL\n",
      "61     FIORELLO H. LAGUARDIA HIGH SCHOOL OF MUSIC & A...\n",
      "302                          TOWNSEND HARRIS HIGH SCHOOL\n",
      "Name: SCHOOL NAME, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(combined[(combined[\"female_per\"] > 60) & (combined[\"sat_score\"] > 1700)][\"SCHOOL NAME\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These schools appear to be very selective liberal arts schools that have high academic standards."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AP Exam Scores vs. SAT Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdf5fc2160>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined[\"ap_per\"] = combined[\"AP Test Takers \"] / combined[\"total_enrollment\"]\n",
    "combined = combined.copy()\n",
    "combined.plot.scatter(x='ap_per', y='sat_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there is a relationship between the percentage of students in a school who take the AP exam and their average SAT scores. It's not a very strong correlation, however."
   ]
  }
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