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acstrahl/NYC_School_Data_Demo.ipynb

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NYC_School_Data_Demo.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": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"combined.to_csv(\"the_dataset.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>DBN</th>\n",
" <th>SCHOOL NAME</th>\n",
" <th>Num of SAT Test Takers</th>\n",
" <th>SAT Critical Reading Avg. Score</th>\n",
" <th>SAT Math Avg. Score</th>\n",
" <th>SAT Writing Avg. Score</th>\n",
" <th>sat_score</th>\n",
" <th>SchoolName</th>\n",
" <th>AP Test Takers</th>\n",
" <th>Total Exams Taken</th>\n",
" <th>...</th>\n",
" <th>priority05</th>\n",
" <th>priority06</th>\n",
" <th>priority07</th>\n",
" <th>priority08</th>\n",
" <th>priority09</th>\n",
" <th>priority10</th>\n",
" <th>Location 1</th>\n",
" <th>lat</th>\n",
" <th>lon</th>\n",
" <th>school_dist</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>01M292</td>\n",
" <td>HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES</td>\n",
" <td>29</td>\n",
" <td>355.0</td>\n",
" <td>404.0</td>\n",
" <td>363.0</td>\n",
" <td>1122.0</td>\n",
" <td>0</td>\n",
" <td>129.028846</td>\n",
" <td>197.038462</td>\n",
" <td>...</td>\n",
" <td>Then to New York City residents</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>220 Henry Street\\nNew York, NY 10002\\n(40.7137...</td>\n",
" <td>40.713764</td>\n",
" <td>-73.985260</td>\n",
" <td>01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>01M448</td>\n",
" <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
" <td>91</td>\n",
" <td>383.0</td>\n",
" <td>423.0</td>\n",
" <td>366.0</td>\n",
" <td>1172.0</td>\n",
" <td>UNIVERSITY NEIGHBORHOOD H.S.</td>\n",
" <td>39.000000</td>\n",
" <td>49.000000</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>200 Monroe Street\\nNew York, NY 10002\\n(40.712...</td>\n",
" <td>40.712332</td>\n",
" <td>-73.984797</td>\n",
" <td>01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>01M450</td>\n",
" <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
" <td>70</td>\n",
" <td>377.0</td>\n",
" <td>402.0</td>\n",
" <td>370.0</td>\n",
" <td>1149.0</td>\n",
" <td>EAST SIDE COMMUNITY HS</td>\n",
" <td>19.000000</td>\n",
" <td>21.000000</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>420 East 12 Street\\nNew York, NY 10009\\n(40.72...</td>\n",
" <td>40.729783</td>\n",
" <td>-73.983041</td>\n",
" <td>01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>01M509</td>\n",
" <td>MARTA VALLE HIGH SCHOOL</td>\n",
" <td>44</td>\n",
" <td>390.0</td>\n",
" <td>433.0</td>\n",
" <td>384.0</td>\n",
" <td>1207.0</td>\n",
" <td>0</td>\n",
" <td>129.028846</td>\n",
" <td>197.038462</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>145 Stanton Street\\nNew York, NY 10002\\n(40.72...</td>\n",
" <td>40.720569</td>\n",
" <td>-73.985673</td>\n",
" <td>01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>01M539</td>\n",
" <td>NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ...</td>\n",
" <td>159</td>\n",
" <td>522.0</td>\n",
" <td>574.0</td>\n",
" <td>525.0</td>\n",
" <td>1621.0</td>\n",
" <td>NEW EXPLORATIONS SCI,TECH,MATH</td>\n",
" <td>255.000000</td>\n",
" <td>377.000000</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>111 Columbia Street\\nNew York, NY 10002\\n(40.7...</td>\n",
" <td>40.718725</td>\n",
" <td>-73.979426</td>\n",
" <td>01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 160 columns</p>\n",
"</div>"
],
"text/plain": [
" DBN SCHOOL NAME \\\n",
"0 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES \n",
"1 01M448 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL \n",
"2 01M450 EAST SIDE COMMUNITY SCHOOL \n",
"3 01M509 MARTA VALLE HIGH SCHOOL \n",
"4 01M539 NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ... \n",
"\n",
" Num of SAT Test Takers SAT Critical Reading Avg. Score \\\n",
"0 29 355.0 \n",
"1 91 383.0 \n",
"2 70 377.0 \n",
"3 44 390.0 \n",
"4 159 522.0 \n",
"\n",
" SAT Math Avg. Score SAT Writing Avg. Score sat_score \\\n",
"0 404.0 363.0 1122.0 \n",
"1 423.0 366.0 1172.0 \n",
"2 402.0 370.0 1149.0 \n",
"3 433.0 384.0 1207.0 \n",
"4 574.0 525.0 1621.0 \n",
"\n",
" SchoolName AP Test Takers Total Exams Taken ... \\\n",
"0 0 129.028846 197.038462 ... \n",
"1 UNIVERSITY NEIGHBORHOOD H.S. 39.000000 49.000000 ... \n",
"2 EAST SIDE COMMUNITY HS 19.000000 21.000000 ... \n",
"3 0 129.028846 197.038462 ... \n",
"4 NEW EXPLORATIONS SCI,TECH,MATH 255.000000 377.000000 ... \n",
"\n",
" priority05 priority06 priority07 priority08 \\\n",
"0 Then to New York City residents 0 0 0.0 \n",
"1 0 0 0 0.0 \n",
"2 0 0 0 0.0 \n",
"3 0 0 0 0.0 \n",
"4 0 0 0 0.0 \n",
"\n",
" priority09 priority10 Location 1 \\\n",
"0 0.0 0.0 220 Henry Street\\nNew York, NY 10002\\n(40.7137... \n",
"1 0.0 0.0 200 Monroe Street\\nNew York, NY 10002\\n(40.712... \n",
"2 0.0 0.0 420 East 12 Street\\nNew York, NY 10009\\n(40.72... \n",
"3 0.0 0.0 145 Stanton Street\\nNew York, NY 10002\\n(40.72... \n",
"4 0.0 0.0 111 Columbia Street\\nNew York, NY 10002\\n(40.7... \n",
"\n",
" lat lon school_dist \n",
"0 40.713764 -73.985260 01 \n",
"1 40.712332 -73.984797 01 \n",
"2 40.729783 -73.983041 01 \n",
"3 40.720569 -73.985673 01 \n",
"4 40.718725 -73.979426 01 \n",
"\n",
"[5 rows x 160 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"combined.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"priority08\n",
"0.0 363\n",
"Name: count, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"combined[\"priority08\"].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Find correlations"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 5 negative correlations:\n",
" frl_percent -0.722225\n",
"sped_percent -0.448170\n",
"ell_percent -0.398750\n",
"hispanic_per -0.396985\n",
"black_per -0.284139\n",
"Name: sat_score, dtype: float64\n",
"\n",
"Top 10 positive correlations:\n",
" sat_score 1.000000\n",
"SAT Writing Avg. Score 0.987771\n",
"SAT Critical Reading Avg. Score 0.986820\n",
"SAT Math Avg. Score 0.972643\n",
"white_per 0.620718\n",
"asian_per 0.570730\n",
"AP Test Takers 0.523140\n",
"Total Exams Taken 0.514333\n",
"asian_num 0.475445\n",
"Number of Exams with scores 3 4 or 5 0.463245\n",
"Name: sat_score, dtype: float64\n"
]
}
],
"source": [
"correlations = combined.corr(numeric_only=True)\n",
"correlations_asc = correlations[\"sat_score\"].sort_values()\n",
"correlations_desc = correlations['sat_score'].sort_values(ascending=False)\n",
"print('Top 5 negative correlations:\\n',correlations_asc.head())\n",
"print('\\nTop 10 positive correlations:\\n',correlations_desc.head(10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting survey correlations"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"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": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.lines.Line2D at 0x7d5e891b6350>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"combined.corr(numeric_only=True)[\"sat_score\"][survey_fields].sort_values().plot.barh()\n",
"plt.axvline(0.25,linestyle=':')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Looking at student-perceived safety versus SAT scores:\n",
"\n",
"combined.plot.scatter('sat_score','saf_s_11')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Safety and SAT Scores"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>saf_s_11</th>\n",
" <th>saf_p_11</th>\n",
" <th>saf_t_11</th>\n",
" <th>saf_tot_11</th>\n",
" <th>sat_score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>boro</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Bronx</th>\n",
" <td>6.606577</td>\n",
" <td>8.346237</td>\n",
" <td>7.026882</td>\n",
" <td>7.322581</td>\n",
" <td>1157.598203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brooklyn</th>\n",
" <td>6.370755</td>\n",
" <td>8.036792</td>\n",
" <td>6.985849</td>\n",
" <td>7.129245</td>\n",
" <td>1181.364461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Manhattan</th>\n",
" <td>6.831370</td>\n",
" <td>8.288889</td>\n",
" <td>7.287778</td>\n",
" <td>7.473333</td>\n",
" <td>1278.331410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Queens</th>\n",
" <td>6.721875</td>\n",
" <td>8.098437</td>\n",
" <td>7.365625</td>\n",
" <td>7.387500</td>\n",
" <td>1286.753032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Staten Island</th>\n",
" <td>6.530000</td>\n",
" <td>7.800000</td>\n",
" <td>7.210000</td>\n",
" <td>7.200000</td>\n",
" <td>1382.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" saf_s_11 saf_p_11 saf_t_11 saf_tot_11 sat_score\n",
"boro \n",
"Bronx 6.606577 8.346237 7.026882 7.322581 1157.598203\n",
"Brooklyn 6.370755 8.036792 6.985849 7.129245 1181.364461\n",
"Manhattan 6.831370 8.288889 7.287778 7.473333 1278.331410\n",
"Queens 6.721875 8.098437 7.365625 7.387500 1286.753032\n",
"Staten Island 6.530000 7.800000 7.210000 7.200000 1382.500000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"borough_safety = combined.groupby('boro').mean(numeric_only=True)\n",
"display(borough_safety[['saf_s_11','saf_p_11','saf_t_11','saf_tot_11','sat_score']].sort_values(by='sat_score'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Race and SAT Scores"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: >"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 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": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Hispanic SAT score scatter plot:\n",
"import numpy as np\n",
"\n",
"combined.plot.scatter('hispanic_per','sat_score')\n",
"# Add trend line\n",
"x = combined['hispanic_per']\n",
"y = combined['sat_score']\n",
"slope, intercept = np.polyfit(x, y, 1) # Fit a line to the data\n",
"plt.plot(x, slope*x + intercept, color='red', label='Trend Line')\n",
"\n",
"plt.title('SAT Scores by Percent of Hispanic Students')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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>SCHOOL NAME</th>\n",
" <th>Num of SAT Test Takers</th>\n",
" <th>hispanic_per</th>\n",
" <th>sat_score</th>\n",
" <th>sped_percent</th>\n",
" <th>ell_percent</th>\n",
" <th>school_dist</th>\n",
" <th>boro</th>\n",
" <th>saf_s_11</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>MANHATTAN BRIDGES HIGH SCHOOL</td>\n",
" <td>66</td>\n",
" <td>99.8</td>\n",
" <td>1058.0</td>\n",
" <td>1.7</td>\n",
" <td>72.6</td>\n",
" <td>02</td>\n",
" <td>Manhattan</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>WASHINGTON HEIGHTS EXPEDITIONARY LEARNING SCHOOL</td>\n",
" <td>70</td>\n",
" <td>96.7</td>\n",
" <td>1174.0</td>\n",
" <td>18.1</td>\n",
" <td>19.6</td>\n",
" <td>06</td>\n",
" <td>Manhattan</td>\n",
" <td>7.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>GREGORIO LUPERON HIGH SCHOOL FOR SCIENCE AND M...</td>\n",
" <td>56</td>\n",
" <td>99.8</td>\n",
" <td>1014.0</td>\n",
" <td>0.0</td>\n",
" <td>89.6</td>\n",
" <td>06</td>\n",
" <td>Manhattan</td>\n",
" <td>7.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>ACADEMY FOR LANGUAGE AND TECHNOLOGY</td>\n",
" <td>54</td>\n",
" <td>99.4</td>\n",
" <td>951.0</td>\n",
" <td>0.9</td>\n",
" <td>86.6</td>\n",
" <td>09</td>\n",
" <td>Bronx</td>\n",
" <td>7.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td>INTERNATIONAL SCHOOL FOR LIBERAL ARTS</td>\n",
" <td>49</td>\n",
" <td>99.8</td>\n",
" <td>934.0</td>\n",
" <td>4.6</td>\n",
" <td>79.9</td>\n",
" <td>10</td>\n",
" <td>Bronx</td>\n",
" <td>7.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE</td>\n",
" <td>30</td>\n",
" <td>99.8</td>\n",
" <td>970.0</td>\n",
" <td>0.2</td>\n",
" <td>92.9</td>\n",
" <td>12</td>\n",
" <td>Bronx</td>\n",
" <td>6.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>253</th>\n",
" <td>MULTICULTURAL HIGH SCHOOL</td>\n",
" <td>29</td>\n",
" <td>99.8</td>\n",
" <td>887.0</td>\n",
" <td>1.0</td>\n",
" <td>94.6</td>\n",
" <td>19</td>\n",
" <td>Brooklyn</td>\n",
" <td>7.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>PAN AMERICAN INTERNATIONAL HIGH SCHOOL</td>\n",
" <td>55</td>\n",
" <td>100.0</td>\n",
" <td>951.0</td>\n",
" <td>0.8</td>\n",
" <td>91.3</td>\n",
" <td>24</td>\n",
" <td>Queens</td>\n",
" <td>7.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SCHOOL NAME Num of SAT Test Takers \\\n",
"44 MANHATTAN BRIDGES HIGH SCHOOL 66 \n",
"82 WASHINGTON HEIGHTS EXPEDITIONARY LEARNING SCHOOL 70 \n",
"89 GREGORIO LUPERON HIGH SCHOOL FOR SCIENCE AND M... 56 \n",
"125 ACADEMY FOR LANGUAGE AND TECHNOLOGY 54 \n",
"141 INTERNATIONAL SCHOOL FOR LIBERAL ARTS 49 \n",
"176 PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE 30 \n",
"253 MULTICULTURAL HIGH SCHOOL 29 \n",
"286 PAN AMERICAN INTERNATIONAL HIGH SCHOOL 55 \n",
"\n",
" hispanic_per sat_score sped_percent ell_percent school_dist \\\n",
"44 99.8 1058.0 1.7 72.6 02 \n",
"82 96.7 1174.0 18.1 19.6 06 \n",
"89 99.8 1014.0 0.0 89.6 06 \n",
"125 99.4 951.0 0.9 86.6 09 \n",
"141 99.8 934.0 4.6 79.9 10 \n",
"176 99.8 970.0 0.2 92.9 12 \n",
"253 99.8 887.0 1.0 94.6 19 \n",
"286 100.0 951.0 0.8 91.3 24 \n",
"\n",
" boro saf_s_11 \n",
"44 Manhattan 7.2 \n",
"82 Manhattan 7.5 \n",
"89 Manhattan 7.7 \n",
"125 Bronx 7.5 \n",
"141 Bronx 7.4 \n",
"176 Bronx 6.8 \n",
"253 Brooklyn 7.1 \n",
"286 Queens 7.3 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"combined[['SCHOOL NAME','Num of SAT Test Takers','hispanic_per','sat_score','sped_percent','ell_percent','school_dist','boro','saf_s_11']][combined['hispanic_per']>95]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>SCHOOL NAME</th>\n",
" <th>Num of SAT Test Takers</th>\n",
" <th>hispanic_per</th>\n",
" <th>sat_score</th>\n",
" <th>sped_percent</th>\n",
" <th>ell_percent</th>\n",
" <th>school_dist</th>\n",
" <th>boro</th>\n",
" <th>saf_s_11</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>STUYVESANT HIGH SCHOOL</td>\n",
" <td>832</td>\n",
" <td>2.4</td>\n",
" <td>2096.0</td>\n",
" <td>0.4</td>\n",
" <td>0.0</td>\n",
" <td>02</td>\n",
" <td>Manhattan</td>\n",
" <td>7.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <td>BRONX HIGH SCHOOL OF SCIENCE</td>\n",
" <td>731</td>\n",
" <td>7.2</td>\n",
" <td>1969.0</td>\n",
" <td>0.1</td>\n",
" <td>0.1</td>\n",
" <td>10</td>\n",
" <td>Bronx</td>\n",
" <td>6.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>BROOKLYN TECHNICAL HIGH SCHOOL</td>\n",
" <td>1277</td>\n",
" <td>7.9</td>\n",
" <td>1833.0</td>\n",
" <td>0.5</td>\n",
" <td>0.1</td>\n",
" <td>13</td>\n",
" <td>Brooklyn</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>327</th>\n",
" <td>QUEENS HIGH SCHOOL FOR THE SCIENCES AT YORK CO...</td>\n",
" <td>121</td>\n",
" <td>7.9</td>\n",
" <td>1868.0</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>28</td>\n",
" <td>Queens</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>356</th>\n",
" <td>STATEN ISLAND TECHNICAL HIGH SCHOOL</td>\n",
" <td>227</td>\n",
" <td>5.3</td>\n",
" <td>1953.0</td>\n",
" <td>0.5</td>\n",
" <td>0.1</td>\n",
" <td>31</td>\n",
" <td>Staten Island</td>\n",
" <td>8.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SCHOOL NAME Num of SAT Test Takers \\\n",
"37 STUYVESANT HIGH SCHOOL 832 \n",
"151 BRONX HIGH SCHOOL OF SCIENCE 731 \n",
"187 BROOKLYN TECHNICAL HIGH SCHOOL 1277 \n",
"327 QUEENS HIGH SCHOOL FOR THE SCIENCES AT YORK CO... 121 \n",
"356 STATEN ISLAND TECHNICAL HIGH SCHOOL 227 \n",
"\n",
" hispanic_per sat_score sped_percent ell_percent school_dist \\\n",
"37 2.4 2096.0 0.4 0.0 02 \n",
"151 7.2 1969.0 0.1 0.1 10 \n",
"187 7.9 1833.0 0.5 0.1 13 \n",
"327 7.9 1868.0 0.2 0.2 28 \n",
"356 5.3 1953.0 0.5 0.1 31 \n",
"\n",
" boro saf_s_11 \n",
"37 Manhattan 7.5 \n",
"151 Bronx 6.8 \n",
"187 Brooklyn 7.0 \n",
"327 Queens 7.2 \n",
"356 Staten Island 8.2 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"combined[['SCHOOL NAME','Num of SAT Test Takers','hispanic_per','sat_score','sped_percent','ell_percent','school_dist','boro','saf_s_11']][(combined['hispanic_per']<10) & (combined['sat_score']>1800)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gender and the SAT"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: >"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 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": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"combined.plot.scatter('sat_score','female_per')\n",
"plt.axhspan(40,60,alpha=0.3)\n",
"plt.title('Percent of Female Students vs. Average SAT Score')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average SAT Score for schools with more than 60% female: 1287.69\n",
"Average SAT Score for schools with less than 40% female: 1188.92\n"
]
}
],
"source": [
"fem_60 = round(combined['sat_score'][combined['female_per']>60].mean(),2)\n",
"fem_40 = round(combined['sat_score'][combined['female_per']<40].mean(),2)\n",
"print(\"Average SAT Score for schools with more than 60% female: \",fem_60)\n",
"print(\"Average SAT Score for schools with less than 40% female: \",fem_40)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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>SCHOOL NAME</th>\n",
" <th>SAT Critical Reading Avg. Score</th>\n",
" <th>SAT Math Avg. Score</th>\n",
" <th>SAT Writing Avg. Score</th>\n",
" <th>sat_score</th>\n",
" <th>female_per</th>\n",
" <th>sped_percent</th>\n",
" <th>ell_percent</th>\n",
" <th>school_dist</th>\n",
" <th>boro</th>\n",
" <th>saf_s_11</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>BARD HIGH SCHOOL EARLY COLLEGE</td>\n",
" <td>624.0</td>\n",
" <td>604.0</td>\n",
" <td>628.0</td>\n",
" <td>1856.0</td>\n",
" <td>68.7</td>\n",
" <td>0.8</td>\n",
" <td>0.2</td>\n",
" <td>01</td>\n",
" <td>Manhattan</td>\n",
" <td>8.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>ELEANOR ROOSEVELT HIGH SCHOOL</td>\n",
" <td>572.0</td>\n",
" <td>594.0</td>\n",
" <td>592.0</td>\n",
" <td>1758.0</td>\n",
" <td>67.5</td>\n",
" <td>1.2</td>\n",
" <td>0.2</td>\n",
" <td>02</td>\n",
" <td>Manhattan</td>\n",
" <td>8.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>BEACON HIGH SCHOOL</td>\n",
" <td>577.0</td>\n",
" <td>575.0</td>\n",
" <td>592.0</td>\n",
" <td>1744.0</td>\n",
" <td>61.0</td>\n",
" <td>3.7</td>\n",
" <td>0.2</td>\n",
" <td>03</td>\n",
" <td>Manhattan</td>\n",
" <td>7.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>FIORELLO H. LAGUARDIA HIGH SCHOOL OF MUSIC &amp; A...</td>\n",
" <td>566.0</td>\n",
" <td>564.0</td>\n",
" <td>577.0</td>\n",
" <td>1707.0</td>\n",
" <td>73.6</td>\n",
" <td>1.0</td>\n",
" <td>0.2</td>\n",
" <td>03</td>\n",
" <td>Manhattan</td>\n",
" <td>7.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>302</th>\n",
" <td>TOWNSEND HARRIS HIGH SCHOOL</td>\n",
" <td>621.0</td>\n",
" <td>651.0</td>\n",
" <td>638.0</td>\n",
" <td>1910.0</td>\n",
" <td>71.1</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>25</td>\n",
" <td>Queens</td>\n",
" <td>7.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SCHOOL NAME \\\n",
"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",
"\n",
" SAT Critical Reading Avg. Score SAT Math Avg. Score \\\n",
"5 624.0 604.0 \n",
"26 572.0 594.0 \n",
"60 577.0 575.0 \n",
"61 566.0 564.0 \n",
"302 621.0 651.0 \n",
"\n",
" SAT Writing Avg. Score sat_score female_per sped_percent ell_percent \\\n",
"5 628.0 1856.0 68.7 0.8 0.2 \n",
"26 592.0 1758.0 67.5 1.2 0.2 \n",
"60 592.0 1744.0 61.0 3.7 0.2 \n",
"61 577.0 1707.0 73.6 1.0 0.2 \n",
"302 638.0 1910.0 71.1 0.2 0.0 \n",
"\n",
" school_dist boro saf_s_11 \n",
"5 01 Manhattan 8.3 \n",
"26 02 Manhattan 8.1 \n",
"60 03 Manhattan 7.8 \n",
"61 03 Manhattan 7.1 \n",
"302 25 Queens 7.8 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"combined[['SCHOOL NAME','SAT Critical Reading Avg. Score','SAT Math Avg. Score',\n",
" 'SAT Writing Avg. Score','sat_score','female_per','sped_percent','ell_percent','school_dist','boro','saf_s_11']][(combined['female_per']>60) & (combined['sat_score']>1700)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## AP vs the SAT"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"combined['ap_per'] = combined['AP Test Takers ']/combined['total_enrollment']\n",
"combined.plot.scatter('sat_score','ap_per', color='red')\n",
"plt.title('Percent of Students Taking an AP Test and Average SAT Score')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note that the vertical line at ~1220 is the dataset mean for SAT score. This value was used to fill in any schools with missing SAT scores."
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>sat_score</th>\n",
" <th>ap_per</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>sat_score</th>\n",
" <td>1.000000</td>\n",
" <td>0.057171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ap_per</th>\n",
" <td>0.057171</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sat_score ap_per\n",
"sat_score 1.000000 0.057171\n",
"ap_per 0.057171 1.000000"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"combined[['sat_score','ap_per']].corr()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0rc1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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