{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Read in the data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "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", "\n", "data = {}\n", "\n", "for f in data_files:\n", " d = pd.read_csv(\"schools/{0}\".format(f))\n", " data[f.replace(\".csv\", \"\")] = d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read in the surveys" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "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[\"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_10\", \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.loc[:,survey_fields]\n", "data[\"survey\"] = survey" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Add DBN columns" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "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": { "collapsed": true }, "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": { "collapsed": true }, "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(numpy.mean)\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": { "collapsed": true }, "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": { "collapsed": true }, "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())\n", "combined = combined.fillna(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Add a school district column for mapping" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_first_two_chars(dbn):\n", " return dbn[0:2]\n", "\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": { "collapsed": false }, "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", "Total Exams Taken 0.514333\n", "Number of Exams with scores 3 4 or 5 0.463245\n", "Total Cohort 0.325144\n", "CSD 0.042948\n", "NUMBER OF STUDENTS / SEATS FILLED 0.394626\n", "NUMBER OF SECTIONS 0.362673\n", "AVERAGE CLASS SIZE 0.381014\n", "SIZE OF SMALLEST CLASS 0.249949\n", "SIZE OF LARGEST CLASS 0.314434\n", "SCHOOLWIDE PUPIL-TEACHER RATIO NaN\n", "schoolyear NaN\n", "fl_percent NaN\n", "frl_percent -0.722225\n", "total_enrollment 0.367857\n", "ell_num -0.153778\n", "ell_percent -0.398750\n", "sped_num 0.034933\n", "sped_percent -0.448170\n", "asian_num 0.475445\n", "asian_per 0.570730\n", "black_num 0.027979\n", "black_per -0.284139\n", "hispanic_num 0.025744\n", "hispanic_per -0.396985\n", "white_num 0.449559\n", " ... \n", "rr_p 0.047925\n", "N_s 0.423463\n", "N_t 0.291463\n", "N_p 0.421530\n", "saf_p_11 0.122913\n", "com_p_11 -0.115073\n", "eng_p_11 0.020254\n", "aca_p_11 0.035155\n", "saf_t_11 0.313810\n", "com_t_11 0.082419\n", "eng_t_10 NaN\n", "aca_t_11 0.132348\n", "saf_s_11 0.337639\n", "com_s_11 0.187370\n", "eng_s_11 0.213822\n", "aca_s_11 0.339435\n", "saf_tot_11 0.318753\n", "com_tot_11 0.077310\n", "eng_tot_11 0.100102\n", "aca_tot_11 0.190966\n", "grade_span_max NaN\n", "expgrade_span_max NaN\n", "zip -0.063977\n", "total_students 0.407827\n", "number_programs 0.117012\n", "priority08 NaN\n", "priority09 NaN\n", "priority10 NaN\n", "lat -0.121029\n", "lon -0.132222\n", "Name: sat_score, dtype: float64\n" ] } ], "source": [ "correlations = combined.corr()\n", "correlations = correlations[\"sat_score\"]\n", "print(correlations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting survey correlations" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "combined.corr()[\"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, as it's hard 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": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "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 thatstrong. 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": [ "# Plotting safety" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lnn6hOTNevhv4PnBYDO/M2c2nn37MBx/87s71jOuZiJoG/NR9ITSwZsW5Pvnk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\nJEniX18tojCgaoYOyZpPx7DrjBk7vtx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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from mpl_toolkits.basemap import Basemap\n", "\n", "districts = combined.groupby(\"school_dist\").agg(numpy.mean)\n", "districts.reset_index(inplace=True)\n", "\n", "m = Basemap(\n", " projection='merc', \n", " llcrnrlat=40.496044, \n", " urcrnrlat=40.915256, \n", " llcrnrlon=-74.255735, \n", " urcrnrlon=-73.700272,\n", " resolution='i'\n", ")\n", "\n", "m.drawmapboundary(fill_color='#85A6D9')\n", "m.drawcoastlines(color='#6D5F47', linewidth=.4)\n", "m.drawrivers(color='#6D5F47', linewidth=.4)\n", "# Temporary bug: if you run the following line of code in the Jupyter Guided Project interface on Dataquest, you'll get an error. \n", "# We're working on a fix, thanks for your patience! This should work fine locally on your own computer though.\n", "# m.fillcontinents(color='white',lake_color='#85A6D9')\n", "\n", "longitudes = districts[\"lon\"].tolist()\n", "latitudes = districts[\"lat\"].tolist()\n", "m.scatter(longitudes, latitudes, s=50, zorder=2, latlon=True, c=districts[\"saf_s_11\"], cmap=\"summer\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like Upper Manhattan and parts of Queens and the Bronx tend to have lower safety scores, whereas Brooklyn has high safety scores." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Racial differences in SAT scores" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "race_fields = [\"white_per\", \"asian_per\", \"black_per\", \"hispanic_per\"]\n", "combined.corr()[\"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 score, 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": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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xtwclzN/R7OdJ+0uOiPuvSp1nYU/PpvP3LduxXuxlRI71Ksq4K5EvAv8hbv9X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\n1OHIfC6BZooAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "combined.plot.scatter(\"hispanic_per\", \"sat_score\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "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 be geared towards recent immigrants to the US. These schools have a lot of students who are learning English, which would explain the lower SAT scores." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "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": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gender_fields = [\"male_per\", \"female_per\"]\n", "combined.corr()[\"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 score, whereas a high percentage of males at a school negatively correlates with SAT score. Neither correlation is extremely strong." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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I3EeOVtJvnzMdXuajFc6sqGASg/jo+w1yzBwa2V4SwFjsJXZCbKtg94lKb8JK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\nQ6U/Z3p0NZYSES2PlIgQdeM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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "combined.plot.scatter(\"female_per\", \"sat_score\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on the scatterplot, 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": 25, "metadata": { "collapsed": false }, "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 appears 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": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3hECAk4ANqfpXAN9q5Uvop1I8c1nhwYZxvBcC6NKzrpF9cNgTYbZ9vBeEzrrS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\n3qcbwuMRMzvC3R82s2cBv4j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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "combined[\"ap_per\"] = combined[\"AP Test Takers \"] / combined[\"total_enrollment\"]\n", "\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 an extremely strong correlation, though." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.5" } }, "nbformat": 4, "nbformat_minor": 0 }