NYC SAT Analysis Pt 2: Visualization and Analysis

Tableau Visualization (Check out my interactive Tableau viz here)

Exploring SAT Scores of New York City: Visualization & Analysis

Part One of this project focused entirely on preparing our data for analysis. While not the most exciting task, it is necessary to some degree for nearly every real-world data set. For the second half of this project, our goal is to understand how differences in school, borough and district relate to a school's mean SAT score and whether these differences actually have any meaningful correlation to the test scores. We will utilize several visualizations, some basic correlation analysis and hypothesis testing to help us discover trends and examine the significance of those trends.

Preliminary Analysis

Up to this point, we haven't set any research questions for our project other than the broad inquiry of whether we can identify any outside factors influencing SAT scores in New York City. Despite having only explored datasets in isolation, we should define some narrower research questions at this point in order to keep our analysis focused. As with all research, we may discover interesting findings outside of our intended scope along the way; however, having concrete questions that we aim to answer will ensure that we do not get lost amidst the numerous possibilities. In this notebook we will explore five potential influencing demographics: sex/gender, cultural, socioeconomic, concentration of advanced/high-achieving students, and physical location of the school.


The research questions we will attempt to answer are as follows:


  • Is a school's average SAT score correlated to... :
    • ...the location of a school (borough or district)?
    • ...the proportion of males to females attending that school?
    • ...any cultural aspects of its student population (e.g., ethnicity, English proficiency)?
    • ...the socioeconomic spread of its students?
    • ...the proportion of high-achieving or advanced placement students?
  • Are any of these correlations strong and significant enough to warrant looking into further?

Setting the Stage with Maps

Just as in our first notebook, we know we are likely to use NumPy and Pandas, so we will start by importing those and setting up Pandas to display up to 500 rows or columns of a table. This time, rather than having to read in nine separate datasets, we only need to take a look at our clean data.

In [1]:
import numpy as np
import pandas as pd

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)

full = pd.read_csv('sat_data_clean.csv')
full.head()
Out[1]:
DBN SCHOOL NAME Num of SAT Test Takers SAT Critical Reading Avg. Score SAT Math Avg. Score SAT Writing Avg. Score sat_score is_suppressed AP Test Takers Total Exams Taken Number of Exams with scores 3 4 or 5 NUMBER OF STUDENTS / SEATS FILLED NUMBER OF SECTIONS AVERAGE CLASS SIZE SIZE OF SMALLEST CLASS SIZE OF LARGEST CLASS schoolyear frl_percent total_enrollment grade9 grade10 grade11 grade12 ell_num ell_percent sped_num sped_percent ctt_num selfcontained_num asian_num asian_per black_num black_per hispanic_num hispanic_per white_num white_per male_num male_per female_num female_per Total Cohort Total Grads - % of cohort Total Regents - % of cohort Total Regents - % of grads Advanced Regents - % of cohort Advanced Regents - % of grads Regents w/o Advanced - % of cohort Regents w/o Advanced - % of grads Local - % of cohort Local - % of grads Still Enrolled - % of cohort Dropped Out - % of cohort borough grade_span_min grade_span_max city postcode total_students school_type language_classes advancedplacement_courses online_ap_courses online_language_courses start_time end_time number_programs Location 1 Community Board Council District lat lon has_lang has_ap has_online_lang has_online_ap has_spanish has_french has_chinese has_russian rr_s rr_t rr_p N_s N_t N_p saf_p_11 com_p_11 eng_p_11 aca_p_11 saf_t_11 com_t_11 eng_t_11 aca_t_11 saf_s_11 com_s_11 eng_s_11 aca_s_11 saf_tot_11 com_tot_11 eng_tot_11 aca_tot_11 school_dist District YTD % Attendance (Avg) YTD Enrollment(Avg) is_consort is_CTE is_allgirls is_intl is_consort_intl is_specialized is_allboys
0 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES 29.0 355.0 404.0 363.0 1122.0 0 0.0 0.0 0.0 65.384615 2.923077 22.700000 20.115385 25.307692 20112012.0 88.6 422.0 98.0 79.0 80.0 50.000000 94.0 22.3 105.0 24.9 34.0 35.0 59.0 14.0 123.0 29.1 227.0 53.8 7.0 1.7 259.0 61.4 163.0 38.6 56.000000 61.500000 41.675000 69.125000 0.000000 0.000000 41.675000 69.125000 19.850000 30.875000 20.275000 11.950000 Manhattan 6.000000 12.0 New York 10002.00000 323.00000 Traditional Chinese (Mandarin), Spanish Psychology Chinese Language and Culture, Spanish Literatu... Chinese (Mandarin), Spanish 830.000000 330.000000 1.000000 220 Henry Street\nNew York, NY 10002\n(40.7137... 3.000000 1.000000 40.713764 -73.985260 1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 89.0 70.0 39.0 379.000000 26.0 151.0 7.8 7.7 7.4 7.6 6.3 5.3 6.1 6.5 6.000000 5.600000 6.100000 6.700000 6.7 6.2 6.6 7.0 1 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
1 01M448 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 91.0 383.0 423.0 366.0 1172.0 0 39.0 49.0 10.0 103.923077 4.423077 23.800000 20.115385 28.000000 20112012.0 71.8 394.0 109.0 97.0 93.0 95.000000 83.0 21.1 86.0 21.8 55.0 10.0 115.0 29.2 89.0 22.6 181.0 45.9 9.0 2.3 226.0 57.4 168.0 42.6 97.714286 60.485714 37.157143 62.471429 8.657143 14.071429 28.528571 48.385714 23.314286 37.528571 26.957143 9.828571 Manhattan 9.000000 12.0 New York 10002.00000 299.00000 Traditional Chinese, Spanish Calculus AB, Chinese Language and Culture, Eng... No courses Chinese (Cantonese), Chinese (Mandarin), Spanish 815.000000 315.000000 3.000000 200 Monroe Street\nNew York, NY 10002\n(40.712... 3.000000 1.000000 40.712332 -73.984797 1.0 1.0 1.0 0.0 1.0 0.0 1.0 0.0 84.0 95.0 10.0 385.000000 37.0 46.0 7.9 7.4 7.2 7.3 6.6 5.8 6.6 7.3 6.000000 5.700000 6.300000 7.000000 6.8 6.3 6.7 7.2 1 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
2 01M450 EAST SIDE COMMUNITY SCHOOL 70.0 377.0 402.0 370.0 1149.0 0 19.0 21.0 0.0 53.535714 2.464286 21.928571 20.464286 23.250000 20112012.0 71.8 598.0 101.0 93.0 77.0 86.000000 30.0 5.0 158.0 26.4 91.0 19.0 58.0 9.7 143.0 23.9 331.0 55.4 62.0 10.4 327.0 54.7 271.0 45.3 79.571429 70.385714 66.000000 93.828571 0.000000 0.000000 66.000000 93.828571 4.357143 6.171429 17.614286 10.742857 Manhattan 6.000000 12.0 New York 10009.00000 649.00000 Consortium School No Language Classes Calculus AB, English Literature and Composition No courses American Sign Language, Arabic, Chinese (Manda... 830.000000 330.000000 1.000000 420 East 12 Street\nNew York, NY 10009\n(40.72... 3.000000 2.000000 40.729783 -73.983041 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 98.0 28.0 516.257511 42.0 150.0 8.7 8.2 8.1 8.4 7.3 8.0 8.0 8.8 6.725751 6.166953 6.719313 7.429828 7.9 7.9 7.9 8.4 1 DISTRICT 01 91.18 12367 1 0 0 0 0 0 0
3 01M458 FORSYTH SATELLITE ACADEMY 7.0 414.0 401.0 359.0 1174.0 0 0.0 0.0 0.0 28.600000 1.200000 23.000000 22.600000 23.400000 20112012.0 72.8 224.0 131.0 49.0 44.0 147.334928 9.0 4.0 20.0 8.9 3.0 0.0 5.0 2.2 77.0 34.4 133.0 59.4 8.0 3.6 97.0 43.3 127.0 56.7 175.547166 62.915625 45.121491 66.876392 10.910809 14.105702 34.210494 52.770587 17.803826 33.140172 24.645990 9.779497 Manhattan 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.719022 -73.982377 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 40.0 100.0 23.0 66.000000 10.0 37.0 8.1 7.0 6.7 7.6 8.5 8.2 8.9 8.9 6.800000 6.100000 6.100000 6.800000 7.8 7.1 7.2 7.8 1 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
4 01M509 MARTA VALLE HIGH SCHOOL 44.0 390.0 433.0 384.0 1207.0 0 0.0 0.0 0.0 49.851852 2.296296 19.370370 17.370370 21.481481 20112012.0 80.7 367.0 143.0 100.0 51.0 73.000000 41.0 11.2 95.0 25.9 28.0 36.0 34.0 9.3 116.0 31.6 209.0 56.9 6.0 1.6 170.0 46.3 197.0 53.7 73.571429 49.914286 31.385714 61.157143 10.571429 19.628571 20.814286 41.514286 18.514286 38.842857 29.857143 14.342857 Manhattan 9.000000 12.0 New York 10002.00000 401.00000 Traditional French, Spanish English Literature and Composition, Studio Art... No courses Spanish 800.000000 330.000000 1.000000 145 Stanton Street\nNew York, NY 10002\n(40.72... 3.000000 1.000000 40.720569 -73.985673 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 90.0 100.0 21.0 306.000000 29.0 69.0 7.7 7.4 7.2 7.3 6.4 5.3 6.1 6.8 6.400000 5.900000 6.400000 7.000000 6.9 6.2 6.6 7.0 1 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0

To this point, we have only worked with numbers and words. However, data is often best understood and communicated through images. Since we have geodata at our disposal, we can draw up some maps to gain a 20,000-foot overview of the schools and school districts that is difficult to picture by simply reading the numbers.

For this task we will use Folium, a Python library that allows us to create interactive maps with the power of the leaflet.js JavaScript library. Our first map will be a map of New York City with a marker identifying each school. If the map is zoomed in, we can see individual schools, while zooming out displays clusters that indicate the number of schools in the area.

In [2]:
import folium
from folium import plugins # Needed for adding a marker cluster & heatmap 

schools_map = folium.Map(location=[full['lat'].mean(), full['lon'].mean()], zoom_start=10)
marker_cluster = plugins.MarkerCluster().add_to(schools_map)
for name, row in full.iterrows():
    # add markers that display DBN and school name when clicked
    folium.Marker([row['lat'], row['lon']], popup='{0}: {1}'.format(row['DBN'], row['SCHOOL NAME'])).add_to(marker_cluster)

schools_map.save('schools.html')
schools_map
Out[2]:

We can also create a heatmap with Folium for a different view of the concentration of schools across the city. Once again, zooming in provides a finer level of detail.

In [3]:
schools_heatmap = folium.Map(location=[full['lat'].mean(), full['lon'].mean()], zoom_start=10)
schools_heatmap.add_child(plugins.HeatMap([[row["lat"], row["lon"]] for name, row in full.iterrows()]))
schools_heatmap.save("heatmap.html")
schools_heatmap
Out[3]:

Now that we have a general idea of where schools are located throughout the city, we can take a closer look at where each district lies. To do this, we will create a map using the GeoJSON file we downloaded at the beginning of our first notebook.

One advantage that Folium provides is the ability to apply a fully-integrated choropleth layer over our map. Using our GeoJSON file to define the district boundaries within the choropleth, we can view values of any column from our data in a color spectrum on our map. However, in order to do this, we need to re-format our data to match the format of our JSON data. Opening up the GeoJSON file we see that each district is indicated by a string containing the (non-zero-padded) district number.

{"type":"Feature","properties":{"school_dist":"6"...

The following code groups our full dataset by the school district, converts our school_dist column into strings and strips the zeros from in front of districts 1 through 9.

In [4]:
district_data = full.groupby('school_dist').mean().reset_index()
district_data['school_dist'] = district_data['school_dist'].apply(lambda x: str(int(x)))
district_data.to_csv('district_data.csv', index=False) # Saving to CSV for later use in Tableau
district_data
Out[4]:
school_dist Num of SAT Test Takers SAT Critical Reading Avg. Score SAT Math Avg. Score SAT Writing Avg. Score sat_score is_suppressed AP Test Takers Total Exams Taken Number of Exams with scores 3 4 or 5 NUMBER OF STUDENTS / SEATS FILLED NUMBER OF SECTIONS AVERAGE CLASS SIZE SIZE OF SMALLEST CLASS SIZE OF LARGEST CLASS schoolyear frl_percent total_enrollment grade9 grade10 grade11 grade12 ell_num ell_percent sped_num sped_percent ctt_num selfcontained_num asian_num asian_per black_num black_per hispanic_num hispanic_per white_num white_per male_num male_per female_num female_per Total Cohort Total Grads - % of cohort Total Regents - % of cohort Total Regents - % of grads Advanced Regents - % of cohort Advanced Regents - % of grads Regents w/o Advanced - % of cohort Regents w/o Advanced - % of grads Local - % of cohort Local - % of grads Still Enrolled - % of cohort Dropped Out - % of cohort grade_span_min grade_span_max postcode total_students start_time end_time number_programs Community Board Council District lat lon has_lang has_ap has_online_lang has_online_ap has_spanish has_french has_chinese has_russian rr_s rr_t rr_p N_s N_t N_p saf_p_11 com_p_11 eng_p_11 aca_p_11 saf_t_11 com_t_11 eng_t_11 aca_t_11 saf_s_11 com_s_11 eng_s_11 aca_s_11 saf_tot_11 com_tot_11 eng_tot_11 aca_tot_11 YTD % Attendance (Avg) YTD Enrollment(Avg) is_consort is_CTE is_allgirls is_intl is_consort_intl is_specialized is_allboys
0 1 73.333333 423.777778 468.444444 413.555556 1305.777778 0.000000 37.444444 52.555556 25.000000 96.341444 4.242394 22.438021 19.084220 25.581993 20112012.0 63.722222 557.222222 120.788457 117.000000 98.666667 100.926103 80.222222 16.422222 59.333333 13.277778 23.777778 11.333333 143.222222 21.388889 106.444444 24.333333 175.555556 40.244444 126.444444 13.255556 272.777778 48.577778 284.444444 51.422222 101.886193 65.130942 52.135483 74.430631 14.394217 19.026348 37.745055 55.399510 13.000901 25.588670 23.471221 9.189309 8.092340 12.0 10244.099368 697.007227 826.528455 333.370370 1.495935 4.260672 8.412352 40.719022 -73.982377 0.555556 0.555556 0.444444 0.111111 0.555556 0.222222 0.444444 0.000000 74.333333 88.666667 36.333333 412.806390 32.111111 210.111111 8.311111 7.711111 7.588889 7.977778 7.511111 6.711111 7.344444 7.966667 7.069528 6.262995 6.835479 7.469981 7.688889 6.955556 7.300000 7.833333 91.18 12367.0 0.111111 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
1 2 95.215385 421.584615 438.292308 416.707692 1276.584615 0.107692 57.800000 90.461538 61.261538 117.518143 4.617674 24.057153 20.407704 27.104752 20112012.0 63.734085 587.791556 168.652068 163.679870 141.849770 132.143762 66.172991 14.974137 69.735521 12.796629 33.063881 13.984510 102.799009 13.738872 143.916752 25.882137 268.596103 48.605874 65.752444 10.462198 267.564821 46.200168 320.226701 53.799819 159.481007 65.260013 47.308477 67.554182 12.139508 14.893343 35.170046 52.661096 17.964940 32.470529 22.565430 9.715520 8.690254 12.0 10185.284178 655.235772 825.242777 317.733333 1.497186 4.838650 7.381429 40.739540 -73.991099 0.738462 0.630769 0.123077 0.123077 0.676923 0.138462 0.076923 0.000000 79.600000 85.061538 37.553846 456.573192 31.507692 183.000000 8.296923 7.578462 7.458462 7.789231 7.444615 6.592308 7.206154 7.627692 7.057319 6.347184 6.804913 7.486613 7.593846 6.824615 7.152308 7.629231 89.01 60823.0 0.107692 0.092308 0.015385 0.015385 0.015385 0.015385 0.000000
2 3 83.647059 419.235294 422.941176 411.647059 1253.823529 0.235294 59.941176 95.588235 55.411765 123.533350 4.830710 22.370030 18.967461 25.351618 20112012.0 59.542092 594.732418 167.535065 162.423032 146.153971 140.667042 40.896732 9.948170 63.518170 14.257699 20.257888 15.941043 61.407974 8.195686 172.858170 34.708170 224.102745 45.628340 127.876993 10.443699 249.806667 47.012405 344.925621 52.987542 180.200594 62.733718 47.259706 69.996154 10.440779 12.987728 36.813394 57.011203 15.472511 30.008920 25.572306 8.886548 8.487578 12.0 10230.283756 733.829906 819.191774 308.326797 1.947394 7.288828 11.128546 40.781574 -73.977370 0.705882 0.588235 0.117647 0.000000 0.647059 0.294118 0.058824 0.000000 76.235294 78.352941 28.764706 425.352941 25.764706 168.710523 8.207187 7.497328 7.479142 7.750270 6.788235 6.041176 6.605882 7.052941 6.776471 6.188235 6.641176 7.335294 7.247059 6.564706 6.923529 7.394118 89.28 21962.0 0.058824 0.058824 0.000000 0.000000 0.000000 0.058824 0.000000
3 4 99.428571 393.142857 405.285714 392.714286 1191.142857 0.000000 55.285714 71.285714 41.428571 93.291096 3.764391 23.987716 20.732331 26.678992 20112012.0 70.342857 532.571429 133.285714 148.000000 119.428571 108.857143 23.857143 6.171429 59.428571 14.942857 26.714286 11.428571 59.000000 6.000000 139.285714 29.514286 321.000000 62.185714 8.857143 1.385714 210.285714 39.371429 322.285714 60.628571 113.755102 67.693878 44.028571 61.207143 11.591837 14.705102 32.444898 46.502041 23.669388 38.792857 19.652041 9.871429 8.493967 12.0 10129.423539 624.860240 832.321719 310.587302 1.260163 10.397431 10.033865 40.793572 -73.942534 0.857143 0.857143 0.142857 0.000000 0.857143 0.142857 0.142857 0.000000 88.857143 92.571429 46.000000 484.142857 31.714286 191.714286 8.185714 7.542857 7.400000 7.800000 7.514286 6.614286 7.014286 7.471429 6.728571 6.014286 6.557143 7.371429 7.471429 6.728571 6.971429 7.542857 91.13 14252.0 0.000000 0.000000 0.142857 0.000000 0.000000 0.000000 0.000000
4 5 63.600000 406.700000 410.000000 400.500000 1217.200000 0.100000 34.800000 42.200000 23.000000 90.196566 3.739961 23.214686 20.062172 26.188577 20112012.0 63.200000 533.400000 130.100000 121.600000 102.630876 93.433493 27.100000 5.820000 59.800000 11.810000 26.538409 18.699773 23.300000 5.230000 307.900000 53.450000 176.700000 35.830000 22.500000 4.870000 258.100000 46.720000 275.300000 53.280000 105.890386 63.161815 48.935012 70.744921 13.426567 16.826736 35.506504 53.919594 14.232075 29.258392 22.605269 10.098399 7.937330 12.0 10238.889431 658.506504 812.875610 327.033333 1.346341 8.634605 12.371117 40.817077 -73.949251 0.700000 0.600000 0.000000 0.000000 0.700000 0.300000 0.000000 0.000000 81.700000 79.200000 38.500000 387.300000 25.400000 165.900000 8.200000 7.470000 7.450000 7.700000 6.850000 6.100000 6.490000 7.080000 6.270000 5.920000 6.380000 7.210000 7.100000 6.510000 6.770000 7.340000 89.08 13170.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.100000 0.000000
5 6 66.818182 382.818182 396.000000 378.272727 1157.090909 0.090909 51.909091 73.545455 26.090909 117.696183 4.579324 24.731261 20.856773 28.028701 20112012.0 82.637778 629.859192 169.281465 157.017412 127.664432 122.060896 158.385859 28.501717 81.255354 14.870990 35.706198 20.726860 17.539596 1.593333 94.689899 13.939899 501.431515 82.480162 12.900808 1.349354 335.883030 53.400990 293.975960 46.598929 168.690270 65.100609 43.938340 64.956802 9.115303 12.377916 34.826848 52.582734 21.152389 35.035143 21.574151 10.324447 8.132524 12.0 10098.905888 641.911062 808.295639 325.010101 1.347376 10.778548 11.861283 40.848970 -73.932502 0.909091 0.818182 0.000000 0.090909 0.909091 0.090909 0.000000 0.000000 83.000000 80.818182 53.181818 450.454545 31.363636 244.909091 8.636364 8.127273 7.900000 8.181818 7.627273 6.800000 7.218182 7.772727 7.090909 6.227273 6.818182 7.600000 7.790909 7.054545 7.309091 7.872727 91.34 25733.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
6 7 49.941176 380.764706 385.411765 375.000000 1141.176471 0.117647 22.941176 35.470588 8.294118 83.143887 3.624158 22.618375 19.326739 25.450079 20112012.0 77.976471 434.764706 105.647059 108.470588 88.764706 88.235294 62.941176 14.517647 77.176471 16.205882 29.058824 27.588235 6.764706 1.494118 134.235294 31.305882 287.705882 65.711765 3.411765 0.805882 241.176471 51.535294 193.588235 48.464706 97.800534 56.699765 35.330119 54.655052 6.278709 8.772800 29.050324 45.885097 21.374092 45.353620 28.415565 10.918708 8.343004 12.0 10516.991711 537.887454 820.588714 314.026144 1.546150 2.889886 15.055778 40.816815 -73.919971 0.705882 0.647059 0.176471 0.176471 0.529412 0.176471 0.000000 0.000000 77.882353 79.235294 40.058824 332.588235 24.705882 157.117647 8.476471 7.817647 7.641176 7.976471 7.017647 6.282353 6.823529 7.341176 6.917647 6.323529 6.811765 7.547059 7.470588 6.800000 7.094118 7.617647 86.75 19717.0 0.117647 0.058824 0.000000 0.058824 0.000000 0.000000 0.000000
7 8 48.380952 387.761905 390.476190 381.571429 1159.809524 0.285714 27.666667 35.428571 10.428571 94.160941 3.866522 22.984436 19.775820 25.689651 20112012.0 75.572910 555.519153 189.304021 146.875384 128.564406 125.429027 56.975661 12.468466 108.124656 18.160085 44.084199 39.618831 30.517672 3.407302 160.389418 31.517989 332.023492 61.955407 28.943704 2.437418 282.163175 47.329608 273.355767 52.670307 162.621731 54.316886 33.923229 56.545056 5.541562 8.147207 28.381401 48.398495 20.398792 43.460466 29.796418 12.762213 8.364863 12.0 10566.224674 626.293973 815.476965 305.947090 1.455672 7.250292 18.947450 40.823004 -73.864576 0.619048 0.476190 0.142857 0.047619 0.619048 0.095238 0.047619 0.000000 67.173860 79.277102 34.702026 355.643572 29.833131 133.098995 8.148675 7.621647 7.497401 7.783552 7.122553 6.326920 6.882695 7.421044 6.654833 6.134948 6.678030 7.331412 7.298531 6.694813 7.004691 7.505218 87.15 31625.0 0.000000 0.000000 0.047619 0.000000 0.000000 0.000000 0.000000
8 9 52.818182 373.772727 381.090909 373.772727 1128.636364 0.045455 19.227273 24.363636 5.818182 80.463122 3.531670 23.223048 19.946652 26.492725 20112012.0 75.631818 426.318182 116.818182 104.136364 81.363636 76.106133 79.681818 20.786364 72.818182 17.350000 24.818182 26.863636 6.500000 1.400000 145.590909 33.690909 268.636364 63.568182 3.045455 0.709091 216.318182 51.513636 209.954545 48.477273 87.078510 66.398787 43.687812 64.303079 5.856128 8.060139 37.825057 56.237082 22.733184 35.721907 21.238717 9.254737 8.268888 12.0 10480.633161 479.001971 811.932003 327.555556 1.211013 3.843820 16.339732 40.836349 -73.906240 0.818182 0.590909 0.045455 0.045455 0.727273 0.227273 0.000000 0.000000 81.000000 86.045455 38.636364 332.772727 25.363636 152.181818 8.522727 7.950000 7.772727 8.095455 6.918182 6.350000 6.918182 7.354545 6.627273 6.145455 6.686364 7.481818 7.350000 6.818182 7.113636 7.663636 89.27 34518.0 0.000000 0.000000 0.000000 0.045455 0.000000 0.000000 0.045455
9 10 98.142857 395.785714 409.928571 391.035714 1196.750000 0.071429 91.714286 158.892857 107.392857 129.446808 5.199036 22.885003 18.959498 26.175094 20112012.0 73.083254 779.496508 213.864008 197.549395 157.379197 139.488209 130.803175 19.797778 100.950635 13.898635 30.255438 39.464042 103.816825 6.212619 174.756349 25.113492 432.660476 62.241556 63.636349 5.842349 393.443810 49.011492 386.052540 50.988444 166.921736 64.184826 47.296412 70.087588 13.485337 16.743680 33.809280 53.347286 16.905776 29.931955 21.222046 11.555591 8.348093 12.0 10519.492451 760.897503 810.268293 331.130952 1.568815 6.953289 14.372227 40.870345 -73.898360 0.714286 0.714286 0.071429 0.071429 0.642857 0.214286 0.035714 0.000000 78.166109 78.600684 36.776520 582.544911 41.053419 255.395675 8.236506 7.666235 7.508765 7.855521 7.052629 6.277333 6.776307 7.372926 6.654491 6.098820 6.686404 7.415351 7.291755 6.660395 6.978518 7.528913 88.92 56757.0 0.000000 0.000000 0.000000 0.035714 0.000000 0.071429 0.000000
10 11 66.894737 388.894737 394.052632 380.263158 1163.210526 0.105263 20.421053 25.894737 5.473684 97.396423 3.930534 24.014070 20.156705 27.446385 20112012.0 67.206901 571.310643 183.746959 161.809635 110.979869 100.140519 66.709942 12.680936 104.874620 17.408515 32.619378 40.841866 30.519532 4.550175 233.640936 40.435673 274.289123 50.013871 28.253567 4.225567 319.443509 55.232725 251.866901 44.767181 131.077772 70.193311 47.324192 65.194708 8.123932 10.432367 39.198686 54.764698 22.866668 34.807036 19.118901 8.205386 8.885845 12.0 10522.255741 607.530880 806.842533 328.233918 1.593924 10.480424 14.365696 40.873138 -73.856120 0.736842 0.684211 0.052632 0.105263 0.736842 0.105263 0.000000 0.000000 79.263158 77.421053 41.631579 376.157895 27.842105 182.894737 8.205263 7.878947 7.747368 7.984211 7.152632 6.947368 7.105263 7.636842 6.400000 5.889474 6.468421 7.363158 7.247368 6.905263 7.110526 7.657895 89.84 38230.0 0.000000 0.052632 0.000000 0.000000 0.000000 0.000000 0.000000
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12 13 122.000000 410.666667 414.333333 401.666667 1226.666667 0.111111 143.666667 237.555556 154.944444 151.051350 5.684546 23.043802 19.782463 25.839729 20112012.0 64.611111 706.500000 191.005339 179.388889 164.388889 160.796385 26.555556 6.744444 53.722222 11.661111 23.653788 19.555303 197.388889 7.833333 345.444444 72.200000 87.722222 15.888889 70.000000 2.877778 380.277778 50.605556 326.222222 49.394444 167.039078 63.127644 47.086836 66.735827 13.897516 16.233728 33.191855 50.495944 16.071524 33.299473 27.448472 7.280075 8.516046 12.0 11073.490214 860.950467 825.736676 334.586420 2.005872 3.772782 30.621405 40.692865 -73.977016 0.722222 0.611111 0.000000 0.111111 0.666667 0.277778 0.055556 0.000000 81.500000 81.888889 34.166667 585.000000 32.722222 244.666667 8.455556 7.833333 7.738889 8.005556 7.127778 6.605556 6.994444 7.533333 6.622222 6.188889 6.711111 7.450000 7.372222 6.850000 7.138889 7.644444 89.56 22785.0 0.000000 0.111111 0.055556 0.000000 0.055556 0.055556 0.000000
13 14 68.687500 395.500000 396.125000 385.250000 1176.875000 0.125000 19.687500 26.625000 2.375000 93.507076 3.775618 23.910637 20.247981 27.255405 20112012.0 74.675972 556.090694 164.818507 138.699471 111.581797 100.208433 56.515278 9.276181 94.488056 17.948806 39.274006 23.124858 24.433472 4.020417 224.349306 43.333681 280.796667 48.186361 22.869306 3.733931 312.607083 55.400681 243.483472 44.586764 98.013783 69.010221 48.789223 67.340068 8.521446 10.286709 40.270792 57.051107 20.234869 32.689824 19.910930 8.258135 8.557221 12.0 11150.183096 573.690210 815.156504 306.576389 1.977642 1.847752 32.217132 40.711599 -73.948360 0.875000 0.687500 0.187500 0.187500 0.812500 0.062500 0.000000 0.000000 77.687500 85.500000 32.250000 372.250000 30.687500 143.750000 8.343750 7.693750 7.612500 7.975000 7.362500 6.743750 7.306250 7.818750 6.775000 6.243750 6.787500 7.543750 7.493750 6.912500 7.256250 7.787500 89.41 20181.0 0.062500 0.062500 0.000000 0.000000 0.000000 0.062500 0.000000
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16 17 62.850000 386.550000 390.850000 375.600000 1153.000000 0.100000 37.700000 57.600000 8.900000 103.903095 4.190471 24.342899 20.161051 27.845455 20112012.0 69.515778 487.322556 133.109611 131.609577 101.580876 102.483493 43.962222 9.700944 53.490444 12.024044 24.219205 16.549886 18.346778 3.376333 398.529444 81.731944 55.237333 11.999089 11.695444 2.117144 216.885667 46.760544 270.436778 53.239411 117.482574 63.808920 45.293792 65.928508 7.040367 9.043677 38.246978 56.878821 18.516025 34.075862 26.395063 7.760450 8.114441 12.0 11096.591192 591.655420 807.063008 306.027778 1.655285 9.645504 33.859264 40.661319 -73.954519 0.700000 0.500000 0.050000 0.150000 0.650000 0.400000 0.050000 0.050000 76.182553 80.140957 38.987128 375.462876 26.524787 176.553945 8.001109 7.677729 7.617271 7.852729 6.873681 6.603266 7.086830 7.522096 6.266288 5.968348 6.560966 7.331491 7.053457 6.749553 7.084926 7.570479 89.67 26551.0 0.000000 0.100000 0.000000 0.050000 0.000000 0.000000 0.000000
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18 19 54.428571 371.928571 382.642857 364.142857 1118.714286 0.142857 20.928571 23.214286 4.785714 79.683736 3.332686 23.189496 19.913765 26.069705 20112012.0 70.915397 480.889365 140.149722 122.299395 101.522054 105.166781 63.017460 16.487063 72.486349 15.005778 29.313149 24.285552 22.066825 3.730476 261.042063 50.788492 182.910476 43.184413 11.707778 1.660206 273.622381 53.286492 207.266825 46.713444 142.420003 59.301244 38.512522 61.047436 6.657942 9.646424 31.855279 51.393577 20.791673 38.976847 26.307751 11.462065 8.708252 12.0 11138.709253 487.788811 804.607433 290.944444 1.903020 5.254574 37.033865 40.676547 -73.882158 0.857143 0.714286 0.357143 0.285714 0.857143 0.071429 0.000000 0.000000 69.428571 86.785714 31.357143 291.571429 27.785714 127.000000 7.657143 7.600000 7.400000 7.692857 6.685714 6.557143 6.900000 7.428571 6.350000 6.021429 6.607143 7.250000 6.900000 6.728571 6.964286 7.442857 87.81 25632.0 0.000000 0.214286 0.000000 0.000000 0.000000 0.000000 0.000000
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20 21 161.923077 396.692308 416.538462 387.923077 1201.153846 0.153846 65.000000 96.692308 37.846154 129.896696 5.116267 23.332248 19.847699 26.019050 20112012.0 64.101197 1050.880855 291.392008 318.476272 202.562212 211.974604 142.941880 15.655299 141.216068 13.798530 59.029545 47.461364 230.995043 15.540513 332.276068 34.626068 221.826667 26.052444 260.992991 23.357145 555.054872 56.254684 495.825812 43.745248 244.760187 59.177335 44.526942 71.661967 12.271824 18.247908 32.248101 53.414759 14.670591 28.360712 27.549957 10.885266 8.455041 12.0 11144.840734 1048.080258 806.038774 303.478632 3.049406 11.966464 43.036470 40.593596 -73.978465 0.846154 0.615385 0.000000 0.076923 0.846154 0.307692 0.230769 0.153846 78.665466 88.832242 35.210966 836.404424 58.115057 305.929145 7.940167 7.450353 7.372724 7.658045 7.082586 6.358871 6.948969 7.480147 6.517365 6.105150 6.578409 7.233064 7.182242 6.637774 6.969116 7.454583 90.50 34342.0 0.000000 0.076923 0.000000 0.076923 0.000000 0.000000 0.000000
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22 23 36.500000 358.666667 375.166667 351.333333 1085.166667 0.000000 14.500000 15.500000 0.000000 85.382105 3.628471 21.879838 18.452419 24.762065 20112012.0 69.683333 362.000000 90.833333 86.500000 55.833333 70.500000 6.166667 1.750000 45.833333 12.600000 15.000000 14.833333 4.166667 1.066667 310.000000 85.216667 44.000000 12.883333 2.000000 0.433333 162.833333 45.983333 199.166667 54.016667 77.222222 44.595833 28.761111 62.800000 4.841667 6.633333 23.920833 56.156944 15.823611 37.211111 43.084722 9.606944 8.228883 12.0 10972.482385 581.510840 834.292683 292.888889 1.577236 11.391008 31.618529 40.668586 -73.912298 0.500000 0.500000 0.000000 0.000000 0.500000 0.166667 0.000000 0.000000 74.166667 71.833333 32.166667 272.666667 18.666667 110.666667 8.350000 7.850000 7.783333 8.183333 6.833333 6.183333 6.916667 7.516667 6.883333 6.416667 7.116667 7.800000 7.366667 6.833333 7.266667 7.833333 86.98 11833.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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24 25 140.000000 423.000000 458.545455 418.090909 1299.636364 0.000000 114.090909 149.818182 85.272727 170.122766 6.560779 25.214998 20.607206 29.085464 20112012.0 55.265051 1106.041010 347.926565 269.653776 234.391705 198.939539 197.658586 16.265354 113.255354 10.398263 38.125826 30.908884 356.448687 32.738788 218.144444 20.712626 403.613333 31.507434 121.173535 14.494808 549.519394 49.310081 556.521414 50.689838 227.016151 60.392776 49.142544 73.835318 16.609758 20.026361 32.543142 53.814782 11.247449 26.167694 28.465829 9.353090 8.033936 12.0 11187.808574 1147.915004 823.250554 304.212121 1.860310 7.667823 21.973743 40.745414 -73.815558 0.636364 0.636364 0.181818 0.000000 0.545455 0.272727 0.363636 0.000000 85.181818 82.000000 37.181818 800.636364 47.363636 290.454545 8.309091 7.681818 7.500000 7.800000 7.463636 6.945455 7.300000 7.772727 7.118182 6.327273 6.936364 7.581818 7.627273 6.990909 7.263636 7.736364 91.90 34371.0 0.000000 0.000000 0.000000 0.090909 0.000000 0.000000 0.000000
25 26 607.800000 445.200000 487.600000 444.800000 1377.600000 0.000000 384.800000 593.000000 361.400000 316.743254 10.672006 26.353720 20.303250 29.495985 20112012.0 43.800000 2991.600000 750.600000 778.000000 751.800000 711.200000 248.600000 7.480000 350.000000 11.960000 112.400000 132.400000 1259.200000 38.140000 676.400000 28.420000 604.800000 19.380000 432.400000 13.420000 1475.000000 49.220000 1516.600000 50.780000 799.476190 76.325238 64.186667 84.060952 28.884762 37.772857 35.311429 46.290952 12.154762 15.976190 15.244762 6.277143 9.000000 12.0 11388.600000 2837.400000 821.000000 337.800000 4.600000 11.800000 21.600000 40.748507 -73.759176 1.000000 1.000000 0.200000 0.200000 1.000000 0.600000 0.600000 0.000000 67.000000 83.400000 23.800000 1930.800000 129.800000 684.200000 7.720000 6.980000 7.220000 7.260000 7.000000 6.580000 6.840000 7.260000 6.760000 6.060000 6.660000 7.380000 7.140000 6.540000 6.900000 7.300000 93.34 31988.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
26 27 145.727273 405.636364 419.818182 390.636364 1216.090909 0.000000 44.545455 60.454545 16.727273 152.182301 5.751941 24.876535 20.841643 27.889288 20112012.0 61.718182 1106.000000 291.090909 276.727273 248.545455 193.181818 114.090909 7.227273 135.909091 12.845455 41.000000 49.454545 240.454545 15.536364 361.545455 40.400000 385.818182 32.554545 98.363636 10.345455 590.909091 52.863636 515.090909 47.136364 294.251825 63.514773 45.475672 66.606675 8.742242 11.789086 36.732063 54.823071 18.053664 33.404543 23.319646 10.610401 8.132524 12.0 11480.814979 1044.729244 791.022912 296.191919 2.438285 11.525638 29.748823 40.638828 -73.807823 0.909091 0.818182 0.363636 0.545455 0.909091 0.090909 0.000000 0.000000 74.090909 81.636364 37.454545 742.272727 55.454545 253.545455 7.645455 7.336364 7.327273 7.590909 6.827273 6.754545 7.063636 7.636364 6.281818 5.909091 6.436364 7.218182 6.881818 6.636364 6.909091 7.463636 89.88 46007.0 0.000000 0.090909 0.000000 0.000000 0.000000 0.000000 0.000000
27 28 157.357143 433.642857 452.428571 423.071429 1309.142857 0.214286 110.714286 164.071429 72.571429 153.625343 5.551129 25.415336 21.096636 28.554577 20112012.0 55.857143 1025.428571 271.714286 260.285714 242.115537 238.500342 81.571429 6.464286 104.857143 9.657143 36.785714 29.714286 337.357143 30.235714 303.714286 39.450000 238.714286 19.864286 132.785714 8.371429 503.142857 46.800000 522.285714 53.200000 314.067801 68.936758 53.568398 73.558366 21.436765 25.814280 32.132798 47.746450 15.370383 26.452127 20.267281 8.807537 8.065395 12.0 11323.280681 1163.360240 806.821719 297.373016 2.331591 8.397431 25.176722 40.709697 -73.805547 0.857143 0.785714 0.071429 0.071429 0.857143 0.285714 0.142857 0.000000 81.903647 81.844225 36.910182 785.589822 48.106839 316.719921 8.137298 7.525327 7.410387 7.753899 7.533830 6.876094 7.074043 7.717280 6.623268 6.026211 6.572808 7.387845 7.433511 6.806505 7.014179 7.629255 91.70 37009.0 0.000000 0.071429 0.071429 0.000000 0.000000 0.071429 0.000000
28 29 49.200000 392.700000 398.000000 382.400000 1173.100000 0.100000 12.000000 18.100000 0.800000 73.998430 2.789649 26.645084 23.522580 29.262070 20112012.0 50.750000 423.300000 134.800000 97.900000 89.630876 86.533493 13.700000 3.270000 57.900000 13.720000 21.200000 16.400000 20.800000 4.920000 355.800000 84.050000 37.900000 8.920000 4.000000 1.010000 221.700000 53.070000 201.600000 46.930000 85.577574 68.779062 51.182506 73.657639 6.652390 9.466046 44.531645 64.199202 17.595383 26.344017 20.313647 9.095807 8.291553 12.0 11276.092954 533.704336 815.250407 299.122222 1.364228 11.156403 27.247411 40.685276 -73.752740 0.800000 0.800000 0.200000 0.200000 0.800000 0.100000 0.000000 0.000000 78.300000 92.500000 43.000000 295.600000 21.200000 142.800000 7.660000 7.310000 7.260000 7.520000 7.280000 7.220000 7.450000 7.830000 5.960000 5.860000 6.340000 7.130000 6.970000 6.790000 7.020000 7.500000 92.14 27232.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
29 30 172.000000 430.333333 465.222222 429.111111 1324.666667 0.111111 114.222222 186.444444 64.000000 167.157553 6.383145 24.426389 19.581769 27.712779 20112012.0 54.211111 1221.555556 327.333333 347.444444 267.333333 247.037214 220.555556 16.133333 125.111111 7.988889 44.931566 49.777525 281.777778 24.866667 124.222222 10.444444 607.222222 43.522222 204.444444 20.788889 604.111111 44.777778 617.444444 55.222222 306.409952 69.621250 59.824352 83.596553 21.146265 27.987934 38.677147 55.606480 9.817782 16.427763 19.245670 9.184438 8.444444 12.0 11103.000000 1123.333333 820.444444 318.333333 2.555556 1.222222 25.111111 40.755398 -73.932306 1.000000 0.777778 0.000000 0.000000 1.000000 0.444444 0.333333 0.000000 89.000000 85.444444 42.333333 963.444444 63.111111 318.777778 8.222222 7.444444 7.433333 7.700000 7.500000 6.655556 6.911111 7.533333 7.033333 6.166667 6.844444 7.511111 7.577778 6.744444 7.077778 7.588889 92.79 39742.0 0.000000 0.111111 0.111111 0.111111 0.000000 0.000000 0.000000
30 31 252.083333 453.833333 462.916667 442.416667 1359.166667 0.000000 158.500000 246.666667 123.666667 198.536039 7.321520 25.297286 20.209668 28.780992 20112012.0 42.209630 1616.287593 473.174676 419.682628 346.442396 321.139155 60.937037 3.659907 261.567407 16.540074 63.365341 91.833144 157.911296 9.510556 280.215741 21.611574 377.895556 25.406815 792.825741 42.945241 827.142778 51.350907 789.144630 48.649019 372.476115 70.470064 55.883344 74.467891 24.279579 29.918014 31.600162 44.543907 14.591908 25.545584 18.834252 7.785472 8.864441 12.0 10376.910795 1668.253613 802.375339 281.685185 4.470190 2.630336 45.372843 40.595680 -74.125726 0.833333 0.833333 0.166667 0.083333 0.666667 0.166667 0.083333 0.083333 88.250000 92.750000 44.083333 1246.500000 76.916667 503.750000 7.958333 7.475000 7.491667 7.733333 7.450000 7.375000 7.616667 8.041667 6.733333 6.150000 6.866667 7.375000 7.391667 7.008333 7.316667 7.716667 90.98 59373.0 0.000000 0.083333 0.000000 0.000000 0.000000 0.083333 0.000000
31 32 52.285714 369.714286 376.000000 361.571429 1107.285714 0.000000 23.428571 29.571429 5.857143 85.597174 3.791643 21.971905 18.397676 25.292561 20112012.0 82.714286 415.285714 113.156587 138.142857 103.329822 114.762133 71.000000 16.628571 63.000000 14.757143 20.285714 27.142857 5.571429 1.542857 89.857143 22.128571 313.142857 74.657143 4.285714 1.085714 210.857143 50.642857 204.428571 49.357143 88.947279 59.155510 30.680000 46.764082 3.501224 5.047347 27.182857 41.711633 28.476531 53.235918 27.725306 11.400408 8.493967 12.0 11159.423539 437.288811 800.893148 325.730159 1.117305 4.397431 34.033865 40.696295 -73.917124 0.857143 0.857143 0.000000 0.000000 0.857143 0.142857 0.000000 0.000000 73.571429 76.285714 35.000000 298.857143 23.000000 135.428571 8.471429 8.057143 7.900000 8.214286 7.214286 6.228571 6.985714 7.557143 6.985714 6.100000 6.900000 7.571429 7.557143 6.785714 7.271429 7.785714 89.28 15297.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
32 75 6.285714 405.000000 417.428571 399.571429 1222.000000 0.857143 0.000000 0.000000 0.000000 117.140546 4.647296 23.595245 19.904134 26.687738 20112012.0 66.515556 710.451111 199.096110 189.191537 155.308756 147.334928 85.244444 13.118889 92.808889 14.080889 34.384091 29.997727 114.935556 9.426667 221.588889 38.638889 276.746667 43.481778 91.908889 7.642889 358.713333 49.510889 351.735556 50.488222 175.547166 62.915625 45.121491 66.876392 10.910809 14.105702 34.210494 52.770587 17.803826 33.140172 24.645990 9.779497 8.457766 12.0 10725.964770 772.021680 816.252033 315.111111 1.821138 6.782016 22.237057 40.735418 -73.928741 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 53.000000 77.857143 27.857143 149.000000 61.285714 102.000000 8.114286 7.685714 7.642857 7.757143 6.771429 6.942857 6.714286 7.271429 6.657143 6.371429 7.328571 7.400000 7.142857 6.985714 7.171429 7.457143 83.21 21435.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
33 79 6.333333 421.333333 394.333333 393.333333 1209.000000 0.333333 0.000000 0.000000 0.000000 117.140546 4.647296 23.595245 19.904134 26.687738 20112012.0 66.515556 710.451111 199.096110 189.191537 155.308756 147.334928 85.244444 13.118889 92.808889 14.080889 34.384091 29.997727 114.935556 9.426667 221.588889 38.638889 276.746667 43.481778 91.908889 7.642889 358.713333 49.510889 351.735556 50.488222 175.547166 62.915625 45.121491 66.876392 10.910809 14.105702 34.210494 52.770587 17.803826 33.140172 24.645990 9.779497 8.457766 12.0 10725.964770 772.021680 816.252033 315.111111 1.821138 6.782016 22.237057 40.775291 -73.900635 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 77.651064 82.819149 36.742553 516.257511 36.495745 213.078891 8.222175 7.654584 7.545416 7.854584 7.173617 6.565319 7.036596 7.541915 6.725751 6.166953 6.719313 7.429828 7.369149 6.791064 7.098511 7.609574 63.81 7288.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

For our first school district choropleth map we will compare SAT scores among districts. Because we may later want to compare a different feature (and creating a map from scratch each time is somewhat time-consuming) we will write a function that can be reused for any column of district_data.

In [5]:
# Takes any column of district_data and returns a district choropleth map with that column 
def show_district_map(col):
    geo_path = 'districts.geojson'
    districts = folium.Map(location=[full['lat'].mean(), full['lon'].mean()], zoom_start=10)
    choropleth = folium.features.Choropleth(
        geo_data=geo_path,
        data=district_data,
        columns=['school_dist', col],
        key_on='feature.properties.school_dist', # found in GeoJSON file
        fill_color='YlGn',
        fill_opacity=0.7,
        line_opacity=0.2,
        legend_name='Average {}'.format(col),
        highlight=True
    ).add_to(districts)
    dist_markers = district_data.iloc[:32] # to avoid physical markers for Districts 75 and 79
    for name, row in dist_markers.iterrows(): # adds circle marker with hover displaying district
        folium.CircleMarker(
            location=[row['lat'], row['lon']], 
            tooltip='{0}: {1}'.format('District', row['school_dist']),
            radius=2).add_to(districts)
    return districts

show_district_map('sat_score')
Out[5]: