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Finding Trends in NYC High School Performance Data
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Finding Trends in NYC High School Performance Data\n", | |
"\n", | |
"We've just received several spreadsheets of information on New York City High Schools. We want to look through this data and see what conclusions we can draw about *the performance of the 9-12 grades in these schools* so far. \n", | |
"\n", | |
"## 1 . Setup & read .csv files\n", | |
"\n", | |
"Before we analyze our data, we need to take care of some basics. We'll import some helpful libraries, like pandas, to load our .csvs into easily usable dataframes. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 31, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
" dbn school_name boro \\\n", | |
"0 17K548 Brooklyn School for Music & Theatre Brooklyn \n", | |
"1 09X543 High School for Violin and Dance Bronx \n", | |
"2 09X327 Comprehensive Model School Project M.S. 327 Bronx \n", | |
"3 02M280 Manhattan Early College School for Advertising Manhattan \n", | |
"4 28Q680 Queens Gateway to Health Sciences Secondary Sc... Queens \n", | |
"\n", | |
" building_code phone_number fax_number grade_span_min grade_span_max \\\n", | |
"0 K440 718-230-6250 718-230-6262 9 12 \n", | |
"1 X400 718-842-0687 718-589-9849 9 12 \n", | |
"2 X240 718-294-8111 718-294-8109 6 12 \n", | |
"3 M520 718-935-3477 NaN 9 10 \n", | |
"4 Q695 718-969-3155 718-969-3552 6 12 \n", | |
"\n", | |
" expgrade_span_min expgrade_span_max ... \\\n", | |
"0 NaN NaN ... \n", | |
"1 NaN NaN ... \n", | |
"2 NaN NaN ... \n", | |
"3 9 14.0 ... \n", | |
"4 NaN NaN ... \n", | |
"\n", | |
" priority02 \\\n", | |
"0 Then to New York City residents \n", | |
"1 Then to New York City residents who attend an ... \n", | |
"2 Then to Bronx students or residents who attend... \n", | |
"3 Then to New York City residents who attend an ... \n", | |
"4 Then to Districts 28 and 29 students or residents \n", | |
"\n", | |
" priority03 \\\n", | |
"0 NaN \n", | |
"1 Then to Bronx students or residents \n", | |
"2 Then to New York City residents who attend an ... \n", | |
"3 Then to Manhattan students or residents \n", | |
"4 Then to Queens students or residents \n", | |
"\n", | |
" priority04 priority05 \\\n", | |
"0 NaN NaN \n", | |
"1 Then to New York City residents NaN \n", | |
"2 Then to Bronx students or residents Then to New York City residents \n", | |
"3 Then to New York City residents NaN \n", | |
"4 Then to New York City residents NaN \n", | |
"\n", | |
" priority06 priority07 priority08 priority09 priority10 \\\n", | |
"0 NaN NaN NaN NaN NaN \n", | |
"1 NaN NaN NaN NaN NaN \n", | |
"2 NaN NaN NaN NaN NaN \n", | |
"3 NaN NaN NaN NaN NaN \n", | |
"4 NaN NaN NaN NaN NaN \n", | |
"\n", | |
" Location 1 \n", | |
"0 883 Classon Avenue\\nBrooklyn, NY 11225\\n(40.67... \n", | |
"1 1110 Boston Road\\nBronx, NY 10456\\n(40.8276026... \n", | |
"2 1501 Jerome Avenue\\nBronx, NY 10452\\n(40.84241... \n", | |
"3 411 Pearl Street\\nNew York, NY 10038\\n(40.7106... \n", | |
"4 160-20 Goethals Avenue\\nJamaica, NY 11432\\n(40... \n", | |
"\n", | |
"[5 rows x 58 columns]\n" | |
] | |
} | |
], | |
"source": [ | |
"#import pandas, numpy, matplotlib, regex\n", | |
"import pandas as pd\n", | |
"import numpy as np\n", | |
"import re\n", | |
"import matplotlib.pyplot as plt\n", | |
"\n", | |
"#load matplotlib inline so we can plot\n", | |
"%matplotlib inline\n", | |
"\n", | |
"# create list of csv files\n", | |
"data_files = [\"ap_2010.csv\",\n", | |
" \"class_size.csv\",\n", | |
" \"demographics.csv\",\n", | |
" \"graduation.csv\",\n", | |
" \"hs_directory.csv\",\n", | |
" \"sat_results.csv\"]\n", | |
"# create an empty dictionary to start\n", | |
"data = {}\n", | |
"# loop through list of .csv files and format into dataframe, then add to 'data' dictionary\n", | |
"for file in data_files:\n", | |
" filename = file.replace('.csv', '')\n", | |
" filepath = 'Data/schools/{0}'.format(file)\n", | |
" data[filename] = pd.read_csv(filepath)\n", | |
" \n", | |
"# print first 5 results from one of the dataframes\n", | |
"print(data['hs_directory'].head())\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 2. Add Survey .txt files\n", | |
"\n", | |
"Everything is working so far. However, we still need to handle a couple files representing school survey answers. These are formatted slightly differently than the other files, so we will read them into datatables with a slightly different method. Then we will remove all but the columns we want to analyze." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 32, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# read survey .txt files, then combine into a single 'survey' dataframe\n", | |
"all_survey = pd.read_csv(\"Data/schools/survey_all.txt\", delimiter=\"\\t\", encoding='windows-1252')\n", | |
"d75_survey = pd.read_csv(\"Data/schools/survey_d75.txt\", delimiter=\"\\t\", encoding='windows-1252')\n", | |
"survey = pd.concat([all_survey, d75_survey], axis=0)\n", | |
"# capitalize 'DBN' column to keep consistent with rest of data\n", | |
"survey[\"DBN\"] = survey[\"dbn\"]\n", | |
"\n", | |
"# create a list of the columns we want to analyze\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", | |
"\n", | |
"# filter out columns that aren't in our list \n", | |
"survey = survey.loc[:,survey_fields]\n", | |
"data[\"survey\"] = survey" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 3. Normalize Class Size data\n", | |
"\n", | |
"We also have class size data in different formats. We will write and run a quick function to make them all appear the same: '05' instead of just '5', for example." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 33, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# make capitalize 'DBN' column to keep consistent with rest of data\n", | |
"data[\"hs_directory\"][\"DBN\"] = data[\"hs_directory\"][\"dbn\"]\n", | |
"\n", | |
"# create a function normalize class size data so that its always 2 digits\n", | |
"# by adding a 0 in front of single digit class sizes\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", | |
"# apply function to unformatted CSD data\n", | |
"data[\"class_size\"][\"padded_csd\"] = data[\"class_size\"][\"CSD\"].apply(pad_csd)\n", | |
"\n", | |
"# combine class size data \n", | |
"data[\"class_size\"][\"DBN\"] = data[\"class_size\"][\"padded_csd\"] + data[\"class_size\"][\"SCHOOL CODE\"]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 4. Convert Columns to Numeric Values\n", | |
"\n", | |
"Our tables contain sat_score data and latitude and longitude values that can be converted to more appropriate data types.\n", | |
"\n", | |
"First we'll handle the sat_results and create a sat_score column from the total scores.\n", | |
"\n", | |
"Then we'll create a couple functions to quickly format lat and long coordinates correctly so that we can convert them to numeric values as well. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 34, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# make a list of columns to convert to numeric data types\n", | |
"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", | |
"# add the math, reading, and writing scores together to get total sat_score\n", | |
"data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]\n", | |
"\n", | |
"# functions to format latitude and longitude data correctly\n", | |
"def find_lat(loc):\n", | |
" coords = re.findall(\"\\(.+, .+\\)\", loc)\n", | |
" lat = coords[0].split(\",\")[0].replace(\"(\", \"\")\n", | |
" return lat\n", | |
"def find_lon(loc):\n", | |
" coords = re.findall(\"\\(.+, .+\\)\", loc)\n", | |
" lon = coords[0].split(\",\")[1].replace(\")\", \"\").strip()\n", | |
" return lon\n", | |
"\n", | |
"# format location data using our functions above, make numeric\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": [ | |
"## 5. Condense Datasets\n", | |
"\n", | |
"We still have a lot of information we don't need. We are only interested in the general education programs for grades 9-12. Furthermore, we want to focus on the 2011-2012 school year." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 35, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"class_size = data[\"class_size\"]\n", | |
"# only keep grades 9-12\n", | |
"class_size = class_size[class_size[\"GRADE \"] == \"09-12\"]\n", | |
"# only keep info for gen ed program types\n", | |
"class_size = class_size[class_size[\"PROGRAM TYPE\"] == \"GEN ED\"]\n", | |
"\n", | |
"class_size = class_size.groupby(\"DBN\").agg(np.mean)\n", | |
"class_size.reset_index(inplace=True)\n", | |
"data[\"class_size\"] = class_size\n", | |
"\n", | |
"# only look at the 2011-2012 school year\n", | |
"data[\"demographics\"] = data[\"demographics\"][data[\"demographics\"][\"schoolyear\"] == 20112012]\n", | |
"data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Cohort\"] == \"2006\"]\n", | |
"data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Demographic\"] == \"Total Cohort\"]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 7. Convert AP scores to numeric values" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 36, | |
"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": [ | |
"## 8. Combine the Datasets" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 37, | |
"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())\n", | |
"combined = combined.fillna(0)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 9. Add a School District Column for Mapping" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 38, | |
"metadata": {}, | |
"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": [ | |
"## 10. Find Correlations" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 39, | |
"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()\n", | |
"correlations = correlations[\"sat_score\"]\n", | |
"print(correlations)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 40, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x7fce3bda0210>" | |
] | |
}, | |
"execution_count": 40, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Remove DBN since it's a unique identifier, not a useful numerical value for correlation.\n", | |
"survey_fields.remove(\"DBN\")\n", | |
"\n", | |
"%matplotlib inline\n", | |
"combined.corr()[\"sat_score\"][survey_fields].plot.bar()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 11. Survey Results\n", | |
"\n", | |
"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.\n", | |
"\n", | |
"Surprisingly, there is a slight negative correlation between how parents score communication. Perhaps if parents get their way TOO much, students suffer? \n", | |
"\n", | |
"## 12. Exploring Safety on SAT Scores" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 46, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.collections.PathCollection at 0x7fce22073f90>" | |
] | |
}, | |
"execution_count": 46, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"plt.scatter(x=combined['saf_s_11'],y=combined['sat_score'])" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"There appears to be a correlation between SAT scores and safety, although it isn't that strong. It looks like there are a few schools with extremely high SAT scores and high safety scores. The correlation is stronger at the low end. 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.\n", | |
"\n", | |
"## 13. Mapping Safe Areas\n", | |
"\n", | |
"We'll look at the average safety score for each distract and map them so we can see the safest areas." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 47, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"0 6.768611\n", | |
"1 6.910660\n", | |
"2 6.716667\n", | |
"3 6.885714\n", | |
"4 6.314286\n", | |
"Name: saf_s_11, dtype: float64\n" | |
] | |
} | |
], | |
"source": [ | |
"#Compute the Average Safety Score for Each District\n", | |
"## groupby school district and find means for columns\n", | |
"district_averages = combined.groupby('school_dist').agg(np.mean)\n", | |
"## reset index\n", | |
"district_averages.reset_index(inplace=True)\n", | |
"print(district_averages['saf_s_11'].head())" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 54, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/jessicadivers/anaconda3/envs/DataViz/lib/python3.7/site-packages/ipykernel_launcher.py:15: MatplotlibDeprecationWarning: \n", | |
"The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n", | |
" from ipykernel import kernelapp as app\n", | |
"/Users/jessicadivers/anaconda3/envs/DataViz/lib/python3.7/site-packages/ipykernel_launcher.py:19: MatplotlibDeprecationWarning: \n", | |
"The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# optional: set location of project espg file to prevent errors\n", | |
"import os \n", | |
"os.environ['PROJ_LIB'] = '/Users/jessicadivers/anaconda3/envs/DataViz/share/proj/'\n", | |
"\n", | |
"#import Basemap for mapping\n", | |
"from mpl_toolkits.basemap import Basemap\n", | |
"\n", | |
"#draw a map of new york using Basemap\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", | |
"m.drawmapboundary(fill_color='#85A6D9')\n", | |
"m.drawcoastlines(color='#6D5F47', linewidth=.4)\n", | |
"m.drawrivers(color='#6D5F47', linewidth=.4)\n", | |
"m.fillcontinents(color='white',lake_color='#85A6D9')\n", | |
"\n", | |
"#get coordinates from district averages data\n", | |
"longitudes = district_averages['lon'].tolist()\n", | |
"latitudes = district_averages['lat'].tolist()\n", | |
"#plot those coordinates on the map\n", | |
"m.scatter(x=longitudes, y=latitudes, s=50, zorder=2, latlon=True, c=district_averages['saf_s_11'], cmap='summer')\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Conclusions:\n", | |
"\n", | |
"\n", | |
"It looks like Upper Manhattan and parts of Queens and the Bronx tend to have higher safety scores, whereas Brooklyn has low safety scores.\n", | |
"\n", | |
"\n", | |
"## 14. Finding Trends in Demographics and SAT Scores" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 55, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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8/bfDVjUae/gjSHIZ8Oqq+ky//VTgXdP45N2uIMlVwGNnh8T6Zxy+WFW/OGxl0ynJV4Ffq6qt/fYvAGdX1SHDVjadkmwBnlxV3+m3fw74XFUdPGxly2cPfzQ3z4Y9QFV9FnBYZ3TXAveas70nXQ9Vo7lhNux71wA3DFXMCrCNn/73fTNw3UC1jMUe/giSvAO4D/DXdGN6vwncBHwEwPHn5UnyUbq5X86jO59H0N2pcwM4R9FyJXk3cCBwJt35fCGwBbgQoKr+Zrjqpk+S9wO/BHyM7nweSzfE8zWAqvqz4apbHgN/BEk+tcjuqqpn7rRiVoAkxy+2v6rO2Fm1rARJTl9kd1XVVI4/D6V/0na7qmpq5i4y8O8BSY43pCYnyUeq6vlD17FSJHlDVf2XoetYKZK8s6p+d+g6lsIx/HvGa4cuYIX5+aELWGFeOHQBK8xThi5gqQz8e4YLeUyWf4ZOlj+fjTLw7xkGlHZl/nw2ysC/Z9iDmizP52R5Pidras6ngX/PuHDoAlaYPxy6gGmS5EELvDd3+u6zdmI5LfjzoQtYKu/SGUGSh9DN5viwqjoqyaHAk6rqfQOXNpWSPIVujYED6ZbdnF0z1Iu1I0hyIXBUVX2/3z4UOLOqHjlsZdMpyXnAC6vqu/32A4EPVdVzh61s+ezhj+Z/0M1A+LB++2vA7w9WzfR7H/BndJOnHQbM9F81mj8FPp5kr36Op7OAlwxc0zTbZzbs4a4FeqZyQRkXMR/NPlV1ZpI3AFTVHUnuHLqoKfa9qvq7oYtYKarq7CR7AOfSrcz0vH7VK43mJ0nWVtU/wV3rBU/l0IiBP5of9hMoza6A80Rg/gpYWrpPJXkb8DfAbbNvOkXF8vQL8cwNor3p5tH53SROUTG6NwGfTfLpfvtXgA0D1jMyx/BH0M/d/k7gkcDlwBq6Mb4vD1rYlNrOVBVOUbFMTlFxz0myD/BEuutLn6+qbw9c0kgM/BEk2RO4EziY7gdgC7BbVd226DdKO0G/5uqtVXVnv70K2LOqbhm2sumS5JCq+mrfwfsZ0/gXqIE/giRfrKrH7eg9LV2/aMcjmDNNclWdOFxF0yvJRcCzq+oH/fZedMvyPXnYyqZLklOrasNK+gvUMfxl6Jc5249ucejHcvcDF3vTTZesEST5K7rz9wzgNLpVhb4waFHT7V6zYQ9QVT/o12HWMlTVhv7rM4auZVIM/OV5LvAyYH+62whn3Qy8cYiCVognV9WjklxWVW9J8na6C7gazQ+TPG52yKG/NfNHA9c01ZI8GVjHnMysqvcPVtCIDPxl6C96nZHk+VX1kaHrWUFmw+iWJA8DvgMctEh7Le73gbOSXN9v70u3SI9GkOQDwC8Al9Jdu4PubigDfyVL8pKq+iCwLsnr5u+fppVvdjGfSPIA4G3AF+n+MZ02bEnTq6ouTnIId99U8NWq+vHAZU2zGeDQWgEXPA385blv/3WvQatYYarqpP7lR5J8gm4M2ucaxnMwcCjdRfDH9vfhT12PdBdxOfBQ4JtDFzIu79LRYJI8s6ouSPIbC+137dXR9EvyPZ0u8DcBRwGfraoXDFnXtOrv0nkM3Y0Ecx8MPGawokZkD38ESdYAr+RnL+K4VujyPA24APj1BfYVXrgd1QuARwNfqqrf6Sf7c4hsdH88dAGTYg9/BEk+B3wGuIS7L+LghVztCpJ8oaoOT3IJ3a2uNwOXV9UjBi5NA7OHP5r7VJVztE9IktcCp9MF03uBxwEnVNW5gxY2vTb3F8HfS9cp+QE+1zCyfq6sdwK/CKwGVgE/rKq9By1sBPbwR5DkT4DPVdWmoWtZCZJ8uaoeneS5wKuB/wic7pPL40uyDti7qi4buJSplWQzcBzdNNMzwEuB9VU1dc/e2MNfhiQ3c/dshG9MchtwR79d0/gbfxcx+8Tyr9IF/ZeTTM2ycbuK7c35MrtvGud+2VVU1dYkq/r5iU7vh3WnjoG/DFV1P7jrQYzPAJ+pqquGrWpFuCTJuXQPW70hyf2Anwxc0zR6+5zXc/90T789dXO/7CJuSbIauDTJW+luz7zvDr5nl+SQzgiSPJNudaZfBn4e+BJd+E/N2pa7kiS70d32tgewJ7APsF9VvXPQwqZUknsDr6L7GS26zsm7q+rWQQubUv2CJ9+iG7//A+D+wLuqauughY3AwB9RP+XsYXR3Qfx74EdVdciwVU2nJK8AXks3R9GldPOOf34aZyPcFSQ5E/g+8D/7t14MPKCqXjRcVdOt7+EfQvcLdEtV3T5wSSNxSGcESc6n+5Pu83S9p8Oq6oZhq5pqr6X75XlRVT2jnxbgLQPXNM0OrqpHz9n+VBIX5xlRP3X3XwFfpxseOyjJv5vGZTldxHw0lwG306149Sjgkf2f0RrNrbPDDUn2rKqv0k0NoNF8qb+VEIAkTwAuHLCeafd24BlV9fSqehrdX/XvGLimkdjDH0FV/QHctbDE79DdQ/5QuvFnLd+2/r7xjwLnJbkJuH4H36N5knyFbshhD+ClSf6p3z4QuHLI2qbcDfPG668BpvIvesfwR5DkNXQXbB8PfAP4v3QXbS8YtLAVIMnT6C6K/f20jpMOpb+4uF1V9Y2dVctKkuTddL80z6T7BfpCumVNL4TpmvPJwB9BktfThfwlVXXHjtpLml5JTl9kd03THFoGviQ1wou2krSIJG9NsneSPZKcn+TbSV4ydF2jMPAlaXHPqarvA78GbAMeDrx+2JJGY+BL0uL26L/+KvDXVfUvQxYzDm/LlKTFfTzJV4EfAa/qF0CaymkqvGgrSTuQ5IHA96vqziT3oZty+p+Hrmu57OFL0gIWWnN53qzdU3P//SwDX5IWNn/N5dnhkNnppqcu8B3SkaRFJLkX8HxgHXd3kquqThysqBHZw5ekxX0U+C7wRe6+WDuVPWV7+JK0iCSXV9Ujh65jErwPX5IW97kkvzR0EZNgD1+SFjBnuundgfV00yLfRn/RtqoeNWB5IzHwJWkBK3G6aQNfkhrhGL4kNcLAl6RGGPiS1AgDX5Ia8f8BZoRqK5WuWsEAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"demographics = ['white_per', 'asian_per', 'black_per', 'hispanic_per']\n", | |
"demographics_corr = combined.corr()['sat_score'][demographics].plot.bar()\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"When we check correlations with school demographics we see white and asian students correlated with better test scores than black and hispanic students. 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.\n", | |
"\n", | |
"Let's dive further into the hispanic numbers since that demographic had the highest negative correlation." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 56, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.collections.PathCollection at 0x7fce22095850>" | |
] | |
}, | |
"execution_count": 56, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"plt.scatter(x=combined['hispanic_per'],y=combined['sat_score'])" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"It seems like once schools exceed 25% of the population being hispanic, the sat scores drop to < 1400." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 57, | |
"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>hispanic_per</th>\n", | |
" <th>sat_score</th>\n", | |
" <th>SAT Critical Reading Avg. Score</th>\n", | |
" <th>SAT Math Avg. Score</th>\n", | |
" <th>SAT Writing Avg. Score</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>44</th>\n", | |
" <td>MANHATTAN BRIDGES HIGH SCHOOL</td>\n", | |
" <td>99.8</td>\n", | |
" <td>1058.0</td>\n", | |
" <td>336.0</td>\n", | |
" <td>378.0</td>\n", | |
" <td>344.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>82</th>\n", | |
" <td>WASHINGTON HEIGHTS EXPEDITIONARY LEARNING SCHOOL</td>\n", | |
" <td>96.7</td>\n", | |
" <td>1174.0</td>\n", | |
" <td>380.0</td>\n", | |
" <td>395.0</td>\n", | |
" <td>399.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>89</th>\n", | |
" <td>GREGORIO LUPERON HIGH SCHOOL FOR SCIENCE AND M...</td>\n", | |
" <td>99.8</td>\n", | |
" <td>1014.0</td>\n", | |
" <td>339.0</td>\n", | |
" <td>349.0</td>\n", | |
" <td>326.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>125</th>\n", | |
" <td>ACADEMY FOR LANGUAGE AND TECHNOLOGY</td>\n", | |
" <td>99.4</td>\n", | |
" <td>951.0</td>\n", | |
" <td>315.0</td>\n", | |
" <td>339.0</td>\n", | |
" <td>297.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>141</th>\n", | |
" <td>INTERNATIONAL SCHOOL FOR LIBERAL ARTS</td>\n", | |
" <td>99.8</td>\n", | |
" <td>934.0</td>\n", | |
" <td>300.0</td>\n", | |
" <td>333.0</td>\n", | |
" <td>301.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>176</th>\n", | |
" <td>PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE</td>\n", | |
" <td>99.8</td>\n", | |
" <td>970.0</td>\n", | |
" <td>321.0</td>\n", | |
" <td>351.0</td>\n", | |
" <td>298.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>253</th>\n", | |
" <td>MULTICULTURAL HIGH SCHOOL</td>\n", | |
" <td>99.8</td>\n", | |
" <td>887.0</td>\n", | |
" <td>279.0</td>\n", | |
" <td>322.0</td>\n", | |
" <td>286.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>286</th>\n", | |
" <td>PAN AMERICAN INTERNATIONAL HIGH SCHOOL</td>\n", | |
" <td>100.0</td>\n", | |
" <td>951.0</td>\n", | |
" <td>317.0</td>\n", | |
" <td>323.0</td>\n", | |
" <td>311.0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" SCHOOL NAME hispanic_per \\\n", | |
"44 MANHATTAN BRIDGES HIGH SCHOOL 99.8 \n", | |
"82 WASHINGTON HEIGHTS EXPEDITIONARY LEARNING SCHOOL 96.7 \n", | |
"89 GREGORIO LUPERON HIGH SCHOOL FOR SCIENCE AND M... 99.8 \n", | |
"125 ACADEMY FOR LANGUAGE AND TECHNOLOGY 99.4 \n", | |
"141 INTERNATIONAL SCHOOL FOR LIBERAL ARTS 99.8 \n", | |
"176 PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE 99.8 \n", | |
"253 MULTICULTURAL HIGH SCHOOL 99.8 \n", | |
"286 PAN AMERICAN INTERNATIONAL HIGH SCHOOL 100.0 \n", | |
"\n", | |
" sat_score SAT Critical Reading Avg. Score SAT Math Avg. Score \\\n", | |
"44 1058.0 336.0 378.0 \n", | |
"82 1174.0 380.0 395.0 \n", | |
"89 1014.0 339.0 349.0 \n", | |
"125 951.0 315.0 339.0 \n", | |
"141 934.0 300.0 333.0 \n", | |
"176 970.0 321.0 351.0 \n", | |
"253 887.0 279.0 322.0 \n", | |
"286 951.0 317.0 323.0 \n", | |
"\n", | |
" SAT Writing Avg. Score \n", | |
"44 344.0 \n", | |
"82 399.0 \n", | |
"89 326.0 \n", | |
"125 297.0 \n", | |
"141 301.0 \n", | |
"176 298.0 \n", | |
"253 286.0 \n", | |
"286 311.0 " | |
] | |
}, | |
"execution_count": 57, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"hispanic_95 = combined[combined['hispanic_per'] >= 95]\n", | |
"hispanic_95[['SCHOOL NAME','hispanic_per', 'sat_score', 'SAT Critical Reading Avg. Score', 'SAT Math Avg. Score',\n", | |
"'SAT Writing Avg. Score']].head(25)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"From a quick google search it seems that most of these schools are catering to immigrant families that have recently arrived in the U.S. This explains why reading and writing scores may be lower for these students.\n", | |
"\n", | |
"Lets look at opposite scenarios. We'll look for high scoring schools with less than 10% hispanic students." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 58, | |
"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", | |
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"</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>hispanic_per</th>\n", | |
" <th>sat_score</th>\n", | |
" <th>SAT Critical Reading Avg. Score</th>\n", | |
" <th>SAT Math Avg. Score</th>\n", | |
" <th>SAT Writing Avg. Score</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>37</th>\n", | |
" <td>STUYVESANT HIGH SCHOOL</td>\n", | |
" <td>2.4</td>\n", | |
" <td>2096.0</td>\n", | |
" <td>679.0</td>\n", | |
" <td>735.0</td>\n", | |
" <td>682.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>151</th>\n", | |
" <td>BRONX HIGH SCHOOL OF SCIENCE</td>\n", | |
" <td>7.2</td>\n", | |
" <td>1969.0</td>\n", | |
" <td>632.0</td>\n", | |
" <td>688.0</td>\n", | |
" <td>649.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>187</th>\n", | |
" <td>BROOKLYN TECHNICAL HIGH SCHOOL</td>\n", | |
" <td>7.9</td>\n", | |
" <td>1833.0</td>\n", | |
" <td>587.0</td>\n", | |
" <td>659.0</td>\n", | |
" <td>587.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>327</th>\n", | |
" <td>QUEENS HIGH SCHOOL FOR THE SCIENCES AT YORK CO...</td>\n", | |
" <td>7.9</td>\n", | |
" <td>1868.0</td>\n", | |
" <td>612.0</td>\n", | |
" <td>660.0</td>\n", | |
" <td>596.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>356</th>\n", | |
" <td>STATEN ISLAND TECHNICAL HIGH SCHOOL</td>\n", | |
" <td>5.3</td>\n", | |
" <td>1953.0</td>\n", | |
" <td>635.0</td>\n", | |
" <td>682.0</td>\n", | |
" <td>636.0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" SCHOOL NAME hispanic_per \\\n", | |
"37 STUYVESANT HIGH SCHOOL 2.4 \n", | |
"151 BRONX HIGH SCHOOL OF SCIENCE 7.2 \n", | |
"187 BROOKLYN TECHNICAL HIGH SCHOOL 7.9 \n", | |
"327 QUEENS HIGH SCHOOL FOR THE SCIENCES AT YORK CO... 7.9 \n", | |
"356 STATEN ISLAND TECHNICAL HIGH SCHOOL 5.3 \n", | |
"\n", | |
" sat_score SAT Critical Reading Avg. Score SAT Math Avg. Score \\\n", | |
"37 2096.0 679.0 735.0 \n", | |
"151 1969.0 632.0 688.0 \n", | |
"187 1833.0 587.0 659.0 \n", | |
"327 1868.0 612.0 660.0 \n", | |
"356 1953.0 635.0 682.0 \n", | |
"\n", | |
" SAT Writing Avg. Score \n", | |
"37 682.0 \n", | |
"151 649.0 \n", | |
"187 587.0 \n", | |
"327 596.0 \n", | |
"356 636.0 " | |
] | |
}, | |
"execution_count": 58, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"hispanic_10 = combined[(combined['sat_score'] >= 1800) & (combined['hispanic_per'] <= 10)]\n", | |
"hispanic_10[['SCHOOL NAME','hispanic_per', 'sat_score', 'SAT Critical Reading Avg. Score', 'SAT Math Avg. Score',\n", | |
"'SAT Writing Avg. Score']].head(25)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"When we look up these schools online, we see that they are specialized schools serving already gifted students. Its not necessarily a surprise that students at these schools are scoring well.\n", | |
"\n", | |
"Lets' move on and look at correlations with student gender." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 59, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"gender_pers = ['female_per', 'male_per']\n", | |
"gender_pers_corr = combined.corr()['sat_score'][gender_pers].plot.bar()\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 60, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"#usual way\n", | |
"#plt.scatter(x=combined['female_per'],y=combined['sat_score'])\n", | |
"\n", | |
"#axis labels are acting buggy in jupyter notebooks\n", | |
"#heres a workaround with x axis labels\n", | |
"fig, ax = plt.subplots()\n", | |
"combined.plot(kind='scatter',x='female_per', y='sat_score', ax=ax)\n", | |
"ax.set_xlabel(\"percentage female\")\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 61, | |
"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>female_per</th>\n", | |
" <th>sat_score</th>\n", | |
" <th>SAT Critical Reading Avg. Score</th>\n", | |
" <th>SAT Math Avg. Score</th>\n", | |
" <th>SAT Writing Avg. Score</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>5</th>\n", | |
" <td>BARD HIGH SCHOOL EARLY COLLEGE</td>\n", | |
" <td>68.7</td>\n", | |
" <td>1856.0</td>\n", | |
" <td>624.0</td>\n", | |
" <td>604.0</td>\n", | |
" <td>628.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>26</th>\n", | |
" <td>ELEANOR ROOSEVELT HIGH SCHOOL</td>\n", | |
" <td>67.5</td>\n", | |
" <td>1758.0</td>\n", | |
" <td>572.0</td>\n", | |
" <td>594.0</td>\n", | |
" <td>592.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>60</th>\n", | |
" <td>BEACON HIGH SCHOOL</td>\n", | |
" <td>61.0</td>\n", | |
" <td>1744.0</td>\n", | |
" <td>577.0</td>\n", | |
" <td>575.0</td>\n", | |
" <td>592.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>61</th>\n", | |
" <td>FIORELLO H. LAGUARDIA HIGH SCHOOL OF MUSIC & A...</td>\n", | |
" <td>73.6</td>\n", | |
" <td>1707.0</td>\n", | |
" <td>566.0</td>\n", | |
" <td>564.0</td>\n", | |
" <td>577.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>302</th>\n", | |
" <td>TOWNSEND HARRIS HIGH SCHOOL</td>\n", | |
" <td>71.1</td>\n", | |
" <td>1910.0</td>\n", | |
" <td>621.0</td>\n", | |
" <td>651.0</td>\n", | |
" <td>638.0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" SCHOOL NAME female_per sat_score \\\n", | |
"5 BARD HIGH SCHOOL EARLY COLLEGE 68.7 1856.0 \n", | |
"26 ELEANOR ROOSEVELT HIGH SCHOOL 67.5 1758.0 \n", | |
"60 BEACON HIGH SCHOOL 61.0 1744.0 \n", | |
"61 FIORELLO H. LAGUARDIA HIGH SCHOOL OF MUSIC & A... 73.6 1707.0 \n", | |
"302 TOWNSEND HARRIS HIGH SCHOOL 71.1 1910.0 \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 \n", | |
"5 628.0 \n", | |
"26 592.0 \n", | |
"60 592.0 \n", | |
"61 577.0 \n", | |
"302 638.0 " | |
] | |
}, | |
"execution_count": 61, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"female_60 = combined[(combined['female_per'] >= 60) & (combined['sat_score'] >= 1700)]\n", | |
"female_60[['SCHOOL NAME','female_per', 'sat_score', 'SAT Critical Reading Avg. Score', 'SAT Math Avg. Score',\n", | |
"'SAT Writing Avg. Score']].head(25)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Conclusions:\n", | |
"It seems like school test scores are only loosely correlated with gender, however, scores do the best when there is more of an even mix between genders than either extreme.\n", | |
"\n", | |
"Looking closer at the highest scoring schools with the most women, we see that they are geberally selective liberal arts schools that have high academic standards.\n", | |
"\n", | |
"\n", | |
"## 15. AP Exam Scores vs SAT Scores\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 62, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.collections.PathCollection at 0x7fce20a2ff50>" | |
] | |
}, | |
"execution_count": 62, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"#calculate percentage of students taking AP exams\n", | |
"combined['AP_per'] = combined['AP Test Takers '] / combined['total_enrollment']\n", | |
"plt.scatter(x=combined['AP_per'],y=combined['sat_score'])" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Conclusions:\n", | |
"\n", | |
"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": "3.7.6" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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