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In [1]:
#CrossCompute
outcome_select = """
Total Grads - % of cohort
Total Grads - % of cohort
Total Grads - n
Total Regents - n
Total Regents - % of cohort
Total Regents - % of grads
Advanced Regents - n
Advanced Regents - % of cohort
Advanced Regents - % of grads
Regents w/o Advanced - n
Regents w/o Advanced - % of cohort
Regents w/o Advanced - % of grads
Local - n
Local - % of cohort
Local - % of grads
Still Enrolled - n
Still Enrolled - % of cohort
Dropped Out - n
Dropped Out - % of cohort
"""
attribute_select = """
diversity_index
diversity_index
"""
# attribute_select = """
# diversity_index
# diversity_index
# economic_need_index
# students_with_disabilities_2
# english_language_learners_2
# female_2
# """
target_folder = '/tmp'
In [2]:
selected_outcome = outcome_select.strip().splitlines()[0]
selected_outcome
selected_attribute = attribute_select.strip().splitlines()[0]
selected_attribute
Out[2]:
'diversity_index'
In [3]:
url = 'https://data.cityofnewyork.us/api/geospatial/r8nu-ymqj?method=export&format=Shapefile'
In [4]:
import geotable
In [5]:
dist_table = geotable.load(url)
In [6]:
dist_table.iloc[0]
Out[6]:
school_dis 16 shape_area 4.67636e+07 shape_leng 35848.9 geometry_object POLYGON ((-73.93311862859143 40.69579115384632... geometry_layer geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b geometry_proj4 +proj=longlat +ellps=WGS84 +no_defs Name: 0, dtype: object
In [7]:
url = 'https://data.cityofnewyork.us/resource/98et-3mve.csv'
In [8]:
import pandas as pd
def load(
endpoint_url,
selected_columns=None,
buffer_size=1000,
search_term_by_column=None,
**kw,
):
buffer_url = (f'{endpoint_url}?$limit={buffer_size}')
if selected_columns:
select_string = ','.join(selected_columns)
buffer_url += f'&$select={select_string}'
for column, search_term in (search_term_by_column or {}).items():
buffer_url += f'&$where={column}+like+"%25{search_term}%25"'
print(buffer_url)
tables = []
if endpoint_url.endswith('.json'):
f = pd.read_json
else:
f = pd.read_csv
t = f(buffer_url, **kw)
while len(t):
print(len(tables) * buffer_size + len(t))
tables.append(t)
offset = buffer_size * len(tables)
t = f(buffer_url + f'&$offset={offset}', **kw)
return pd.concat(tables, ignore_index=True, sort=False)
In [9]:
demo_table = load(url, na_values=['No Data'])
https://data.cityofnewyork.us/resource/98et-3mve.csv?$limit=1000 1000 2000 3000 4000 5000 6000 7000 8000 8972
In [10]:
demo_table = demo_table[demo_table.year == '2016-17']
In [11]:
demo_table.iloc[0]
Out[11]:
asian_1 14 asian_2 7.9 black_1 51 black_2 28.7 dbn 01M015 economic_need_index 89.2% english_language_learners_1 12 english_language_learners_2 6.7 female_1 83 female_2 46.6 grade_1 33 grade_10 0 grade_11 0 grade_12 0 grade_2 27 grade_3 31 grade_4 24 grade_5 18 grade_6 0 grade_7 0 grade_8 0 grade_9 0 grade_k 28 grade_pk_half_day_full_day 17 hispanic_1 105 hispanic_2 59 male_1 95 male_2 53.4 multiple_race_categories_not_represented_1 4 multiple_race_categories_not_represented_2 2.2 poverty_1 152 poverty_2 85.4 school_name P.S. 015 Roberto Clemente students_with_disabilities_1 51 students_with_disabilities_2 28.7 total_enrollment 178 white_1 4 white_2 2.2 year 2016-17 Name: 3, dtype: object
In [12]:
# ADD DIVERSITY INDEX
def d_index(row):
o = row['multiple_race_categories_not_represented_2']
total = 100 - o
target = total / 4
abs_distance = (abs(target - row['asian_2'])) + abs((target - row['black_2'])) + abs((target - row['hispanic_2'])) + abs((target - row['white_2']))
if o == 0.0:
return abs_distance
if o > 0.0:
# diversity index
return abs_distance * (1/o)
In [13]:
demo_table['diversity_index'] = demo_table.apply(d_index, axis=1)
In [14]:
from sklearn import preprocessing
x = demo_table['diversity_index'].values.reshape(-1,1) #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
demo_table['scaled_index'] = pd.DataFrame(x_scaled)
In [15]:
len(demo_table.scaled_index)
Out[15]:
1823
In [16]:
demo_table.scaled_index.describe()
Out[16]:
count 367.000000 mean 0.062403 std 0.084847 min 0.000910 25% 0.015049 50% 0.032192 75% 0.072222 max 0.695898 Name: scaled_index, dtype: float64
In [17]:
demo_table.iloc[0]
Out[17]:
asian_1 14 asian_2 7.9 black_1 51 black_2 28.7 dbn 01M015 economic_need_index 89.2% english_language_learners_1 12 english_language_learners_2 6.7 female_1 83 female_2 46.6 grade_1 33 grade_10 0 grade_11 0 grade_12 0 grade_2 27 grade_3 31 grade_4 24 grade_5 18 grade_6 0 grade_7 0 grade_8 0 grade_9 0 grade_k 28 grade_pk_half_day_full_day 17 hispanic_1 105 hispanic_2 59 male_1 95 male_2 53.4 multiple_race_categories_not_represented_1 4 multiple_race_categories_not_represented_2 2.2 poverty_1 152 poverty_2 85.4 school_name P.S. 015 Roberto Clemente students_with_disabilities_1 51 students_with_disabilities_2 28.7 total_enrollment 178 white_1 4 white_2 2.2 year 2016-17 diversity_index 35.2727 scaled_index 0.096328 Name: 3, dtype: object
In [18]:
selected_attribute
Out[18]:
'diversity_index'
In [19]:
len(demo_table)
if selected_attribute in demo_table:
demo_table = demo_table.dropna(subset=[selected_attribute])
if type(demo_table[selected_attribute]) == str:
demo_table[selected_attribute] = demo_table[selected_attribute].str.replace('%', '')
demo_table[selected_attribute] = demo_table[selected_attribute].astype('float')
In [20]:
selected_attribute
Out[20]:
'diversity_index'
In [21]:
demo_table.year
Out[21]:
3 2016-17
8 2016-17
13 2016-17
18 2016-17
23 2016-17
28 2016-17
33 2016-17
38 2016-17
43 2016-17
48 2016-17
53 2016-17
58 2016-17
63 2016-17
68 2016-17
73 2016-17
78 2016-17
83 2016-17
88 2016-17
93 2016-17
98 2016-17
103 2016-17
109 2016-17
114 2016-17
119 2016-17
124 2016-17
129 2016-17
133 2016-17
138 2016-17
143 2016-17
148 2016-17
...
8839 2016-17
8843 2016-17
8847 2016-17
8851 2016-17
8855 2016-17
8860 2016-17
8863 2016-17
8866 2016-17
8871 2016-17
8876 2016-17
8879 2016-17
8884 2016-17
8887 2016-17
8892 2016-17
8897 2016-17
8902 2016-17
8905 2016-17
8910 2016-17
8915 2016-17
8920 2016-17
8925 2016-17
8927 2016-17
8929 2016-17
8940 2016-17
8945 2016-17
8950 2016-17
8955 2016-17
8960 2016-17
8965 2016-17
8970 2016-17
Name: year, Length: 1823, dtype: object
In [22]:
#demo_table['Diversity Index'] = demo_table['economic_need_index']
In [23]:
endpoint_url = 'https://data.cityofnewyork.us/resource/r2nx-nhxe.csv'
In [24]:
# Load schools
school_location_table = load(
endpoint_url,
buffer_size=1000)
school_location_table[:5]
https://data.cityofnewyork.us/resource/r2nx-nhxe.csv?$limit=1000 1000 1823
Out[24]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<p>5 rows × 48 columns</p>
| :@computed_region_92fq_4b7q | :@computed_region_efsh_h5xi | :@computed_region_f5dn_yrer | :@computed_region_sbqj_enih | :@computed_region_yeji_bk3q | admin_district_location_code | administrative_district_name | ats_system_code | beds_number | borough_block_lot | ... | primary_building_code | principal_name | principal_phone_number | principal_title | school_support_team_leader_name | school_support_team_name | state_code | status_descriptions | x_coordinate | y_coordinate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 50.0 | 11729.0 | 70.0 | 5.0 | 4.0 | M801 | COMMUNITY SCHOOL DISTRICT 01 | 01M015 | 310100010015 | 1003740020 | ... | M015 | IRENE SANCHEZ | 212-228-8730 | PRINCIPAL | NaN | School Support Team 3- Manhattan | NY | Open | 990141.0 | 202349.0 |
| 1 | 50.0 | 11724.0 | 70.0 | 5.0 | 4.0 | M801 | COMMUNITY SCHOOL DISTRICT 01 | 01M019 | 310100010019 | 1004530034 | ... | M019 | JACQUELINE FLANAGAN | 212-533-5340 | PRINCIPAL | NaN | School Support Team 3- Manhattan | NY | Open | 988547.0 | 205239.0 |
| 2 | 32.0 | 11723.0 | 70.0 | 4.0 | 4.0 | M801 | COMMUNITY SCHOOL DISTRICT 01 | 01M020 | 310100010020 | 1003550001 | ... | M020 | SARAH PINTO VIAGRAN | 212-254-9577 | PRINCIPAL | NaN | School Support Team 3- Manhattan | NY | Open | 988044.0 | 202068.0 |
| 3 | 50.0 | 11729.0 | 70.0 | 5.0 | 4.0 | M801 | COMMUNITY SCHOOL DISTRICT 01 | 01M034 | 310100010034 | 1003810038 | ... | M034 | ANGELIKI LOUKATOS | 212-228-4433 | PRINCIPAL | NaN | School Support Team 3- Manhattan | NY | Open | 991163.0 | 203782.0 |
| 4 | 50.0 | 11729.0 | 70.0 | 5.0 | 4.0 | M801 | COMMUNITY SCHOOL DISTRICT 01 | 01M063 | 310100010063 | 1004310014 | ... | M063 | DARLENE CAMERON | 212-674-3180 | PRINCIPAL | NaN | School Support Team 3- Manhattan | NY | Open | 988071.0 | 203210.0 |
In [25]:
school_location_table.iloc[0]
Out[25]:
:@computed_region_92fq_4b7q 50 :@computed_region_efsh_h5xi 11729 :@computed_region_f5dn_yrer 70 :@computed_region_sbqj_enih 5 :@computed_region_yeji_bk3q 4 admin_district_location_code M801 administrative_district_name COMMUNITY SCHOOL DISTRICT 01 ats_system_code 01M015 beds_number 310100010015 borough_block_lot 1003740020 census_tract 2601 community_district 103 community_school_sup_name PHILLIPS, DANIELLA council_district 2 fax_number 212-477-0931 field_support_center_leader_name CHU, YUET field_support_center_name Field Support Center - Manhattan fiscal_year 2018 geographical_district_code 1 grades_final_text PK,0K,01,02,03,04,05 grades_text PK,0K,01,02,03,04,05,SE highschool_network_location_code NaN highschool_network_name NaN highschool_network_superintendent NaN location_1 POINT (-73.978747 40.722075) location_1_address 333 EAST 4 STREET location_1_city MANHATTAN location_1_state NY location_1_zip 10009 location_category_description Elementary location_code M015 location_name P.S. 015 Roberto Clemente location_type_description General Academic managed_by_name DOE nta MN28 nta_name Lower East Side ... open_date 1904-07-01T00:00:00.000 primary_address_line_1 333 EAST 4 STREET primary_building_code M015 principal_name IRENE SANCHEZ principal_phone_number 212-228-8730 principal_title PRINCIPAL school_support_team_leader_name NaN school_support_team_name School Support Team 3- Manhattan state_code NY status_descriptions Open x_coordinate 990141 y_coordinate 202349 Name: 0, dtype: object
In [26]:
school_location_table.iloc[0]['location_1']
Out[26]:
'POINT (-73.978747 40.722075)'
In [27]:
school_location_table.iloc[0]['ats_system_code']
Out[27]:
'01M015 '
In [28]:
school_location_table['DBN'] = school_location_table['ats_system_code'].str.strip()
In [29]:
school_location_table.iloc[0]['DBN']
Out[29]:
'01M015'
In [30]:
school_location_table = school_location_table.rename(columns={
'location_1': 'WKT',
'location_name': 'School Name',
})
In [31]:
trimmed_school_location_table = school_location_table[[
'DBN',
'WKT',
'School Name',
]]
In [32]:
school_table = pd.merge(
trimmed_school_location_table,
demo_table,
left_on='DBN',
right_on='dbn')
len(school_table)
Out[32]:
1802
In [33]:
school_table = school_table.dropna(subset=['WKT'])
In [34]:
len(school_table)
Out[34]:
1801
In [35]:
school_table.iloc[0]
Out[35]:
DBN 01M015 WKT POINT (-73.978747 40.722075) School Name P.S. 015 Roberto Clemente asian_1 14 asian_2 7.9 black_1 51 black_2 28.7 dbn 01M015 economic_need_index 89.2% english_language_learners_1 12 english_language_learners_2 6.7 female_1 83 female_2 46.6 grade_1 33 grade_10 0 grade_11 0 grade_12 0 grade_2 27 grade_3 31 grade_4 24 grade_5 18 grade_6 0 grade_7 0 grade_8 0 grade_9 0 grade_k 28 grade_pk_half_day_full_day 17 hispanic_1 105 hispanic_2 59 male_1 95 male_2 53.4 multiple_race_categories_not_represented_1 4 multiple_race_categories_not_represented_2 2.2 poverty_1 152 poverty_2 85.4 school_name P.S. 015 Roberto Clemente students_with_disabilities_1 51 students_with_disabilities_2 28.7 total_enrollment 178 white_1 4 white_2 2.2 year 2016-17 diversity_index 35.2727 scaled_index 0.096328 Name: 0, dtype: object
In [36]:
dist_table.iloc[0]
Out[36]:
school_dis 16 shape_area 4.67636e+07 shape_leng 35848.9 geometry_object POLYGON ((-73.93311862859143 40.69579115384632... geometry_layer geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b geometry_proj4 +proj=longlat +ellps=WGS84 +no_defs Name: 0, dtype: object
In [37]:
geometry_wkt = school_table.iloc[0]['WKT']
In [38]:
geometry_wkt
Out[38]:
'POINT (-73.978747 40.722075)'
In [39]:
from shapely import wkt
In [40]:
g = wkt.loads(geometry_wkt)
In [41]:
g
Out[41]:
<shapely.geometry.point.Point at 0x7fbaa0032940>
In [42]:
district_polygons = dist_table['geometry_object']
In [43]:
flags = [x.contains(g) for x in district_polygons]
flags
Out[43]:
[False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]
In [44]:
import numpy as np
In [45]:
index = np.argmax(flags)
In [46]:
dist_table.iloc[index]
Out[46]:
school_dis 1 shape_area 3.51607e+07 shape_leng 28641.3 geometry_object POLYGON ((-73.97177410965313 40.72582128133706... geometry_layer geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b geometry_proj4 +proj=longlat +ellps=WGS84 +no_defs Name: 17, dtype: object
In [47]:
index
Out[47]:
17
In [48]:
len(flags)
Out[48]:
33
In [49]:
int(dist_table.iloc[index]['school_dis'])
Out[49]:
1
In [50]:
len(school_table)
Out[50]:
1801
In [51]:
from geotable import ColorfulGeometryCollection, GeoTable
In [52]:
dist_table
Out[52]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| school_dis | shape_area | shape_leng | geometry_object | geometry_layer | geometry_proj4 | |
|---|---|---|---|---|---|---|
| 0 | 16.0 | 4.676362e+07 | 35848.904605 | POLYGON ((-73.93311862859143 40.69579115384632... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 1 | 32.0 | 5.189850e+07 | 37251.057847 | POLYGON ((-73.91180710069435 40.70343495202662... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 2 | 6.0 | 9.634170e+07 | 70447.849084 | POLYGON ((-73.92640556921116 40.87762147653734... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 3 | 31.0 | 1.604472e+09 | 434471.412859 | (POLYGON ((-74.05050806403247 40.5664220341608... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 4 | 7.0 | 9.226247e+07 | 65294.452403 | (POLYGON ((-73.89680883223774 40.7958084451597... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 5 | 23.0 | 4.740069e+07 | 40317.452033 | (POLYGON ((-73.92044366203014 40.6656262871675... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 6 | 29.0 | 4.201981e+08 | 135035.241651 | POLYGON ((-73.73816144093141 40.72895809117297... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 7 | 26.0 | 4.247909e+08 | 125677.678898 | POLYGON ((-73.74344992332192 40.77824115291502... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 8 | 15.0 | 1.961534e+08 | 153439.165680 | POLYGON ((-73.98633135042395 40.69105051012824... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 9 | 17.0 | 1.284414e+08 | 68341.398899 | POLYGON ((-73.92044366203014 40.6656262871675,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 10 | 19.0 | 2.034175e+08 | 184183.167312 | (POLYGON ((-73.846736514711 40.60485301485166,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 11 | 13.0 | 1.048706e+08 | 86635.210559 | POLYGON ((-73.97906084911834 40.70594602894087... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 12 | 18.0 | 1.751488e+08 | 121184.158477 | (POLYGON ((-73.86706149472118 40.5820879767934... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 13 | 20.0 | 2.426965e+08 | 94309.778946 | POLYGON ((-74.02552971543656 40.65147855069281... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 14 | 3.0 | 1.134889e+08 | 52072.051321 | POLYGON ((-73.95671863064405 40.78660079332199... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 15 | 5.0 | 5.251977e+07 | 44469.588221 | POLYGON ((-73.93515659239551 40.83268240623763... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 16 | 9.0 | 8.341539e+07 | 46648.958586 | POLYGON ((-73.9212971968614 40.85428933985649,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 17 | 1.0 | 3.516075e+07 | 28641.276279 | POLYGON ((-73.97177410965313 40.72582128133706... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 18 | 14.0 | 1.503102e+08 | 95792.082090 | POLYGON ((-73.95439555417087 40.73911477252251... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 19 | 4.0 | 5.262043e+07 | 52061.828459 | (POLYGON ((-73.92133752419399 40.8008521064970... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 20 | 10.0 | 2.825415e+08 | 94957.570434 | POLYGON ((-73.86789798628736 40.90294017690526... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 21 | 12.0 | 6.907182e+07 | 48527.595776 | POLYGON ((-73.88284445574813 40.84781722645163... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 22 | 25.0 | 4.436285e+08 | 175827.007127 | POLYGON ((-73.82049919995312 40.80101146781899... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 23 | 28.0 | 2.475679e+08 | 114694.912786 | POLYGON ((-73.84485477879177 40.7357514698091,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 24 | 24.0 | 3.949782e+08 | 127343.703736 | (POLYGON ((-73.90641585511733 40.7398683641967... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 25 | 30.0 | 3.181290e+08 | 150392.978241 | POLYGON ((-73.90647314610101 40.79018117520807... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 26 | 21.0 | 2.101971e+08 | 123858.087345 | POLYGON ((-73.96184657346174 40.62757081710622... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 27 | 22.0 | 3.855533e+08 | 271718.504936 | (POLYGON ((-73.91990064270161 40.5996005215871... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 28 | 27.0 | 7.955970e+08 | 589135.490708 | (POLYGON ((-73.82784008953526 40.5887858248046... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 29 | 11.0 | 3.926651e+08 | 305305.869806 | (POLYGON ((-73.78833349834532 40.8346671297593... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 30 | 8.0 | 2.588266e+08 | 223080.044096 | (POLYGON ((-73.83979488562292 40.8356192069902... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 31 | 2.0 | 2.804004e+08 | 212406.819436 | (POLYGON ((-74.0438776163991 40.69018767537123... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
| 32 | 10.0 | 3.282963e+06 | 7883.372664 | (POLYGON ((-73.9089323517538 40.8721573479701,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs |
In [53]:
dist_table.geometry_object[0].wkt
Out[53]:
'POLYGON ((-73.93311862859143 40.69579115384632, -73.93236605675587 40.69535837729649, -73.9319289006206 40.6951146575314, -73.93177404555487 40.69502821484371, -73.93147184545109 40.69486090242515, -73.93121199886629 40.69471276978224, -73.93114516482393 40.69467397486358, -73.930511465969 40.69431652304853, -73.92970192402746 40.69386339761171, -73.92882897891457 40.69335047407161, -73.92882834168937 40.6933500992537, -73.92864268981751 40.69324101255102, -73.92850659671787 40.69316572635911, -73.92804330687132 40.69290417236994, -73.92781120671985 40.6927742086535, -73.92750685270927 40.69260381145623, -73.92707069461525 40.69234622359373, -73.92644992133326 40.69200176882866, -73.92631612531525 40.69192845396802, -73.92556064122292 40.691490840003, -73.92534903613239 40.69136694183798, -73.92439183303664 40.69083174877439, -73.92429213268012 40.69077335685399, -73.92364379835564 40.6903988375817, -73.92316966710699 40.69012340599072, -73.92280702359515 40.68992392534457, 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-73.93976769964368 40.69646329104629, -73.93948446000253 40.69649454647407, -73.93917199789162 40.69653049512544, -73.93884866773332 40.69656769159932, -73.93867948785814 40.69658715370254, -73.93779110726068 40.69668934223279, -73.93763027449141 40.69595086258837, -73.93755700214285 40.69558451152044, -73.93748376644189 40.6952184321377, -73.9370544081801 40.69553123358047, -73.9348298043286 40.69575489184654, -73.9345601174396 40.69555483174558, -73.93463155014204 40.69592163383321, -73.93470512606679 40.69628830975414, -73.9347969968416 40.69673659235447, -73.93468892593663 40.69667495388458, -73.9346067517839 40.69662808583959, -73.93457999376531 40.69661282447046, -73.93448784825009 40.69656026847871, -73.93413397294013 40.69635843140355, -73.93382817155928 40.69618427730787, -73.93311862859143 40.69579115384632))'
In [54]:
school_table.WKT[0]
Out[54]:
'POINT (-73.978747 40.722075)'
In [55]:
len(dist_table.school_dis.unique())
Out[55]:
32
In [56]:
# aggregate districts
from shapely import wkt
import numpy as np
district_polygons = dist_table['geometry_object']
def get_district(geometry_wkt):
g = wkt.loads(geometry_wkt)
#flags = [x.contains(g) for x in district_polygons]
flags = [x.intersects(g) for x in district_polygons]
index = np.argmax(flags)
district = int(dist_table.iloc[index]['school_dis'])
return district
school_table['district'] = school_table['WKT'].apply(get_district)
In [57]:
len(school_table.district.unique())
Out[57]:
32
In [58]:
len(dist_table)
Out[58]:
33
In [59]:
mean_table = school_table.groupby('district').mean()
In [60]:
mean_table = mean_table.reset_index()
In [61]:
mean_table = mean_table[[
'district',
selected_attribute,
]]
In [62]:
mean_table
Out[62]:
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| district | diversity_index | |
|---|---|---|
| 0 | 1 | 74.392314 |
| 1 | 2 | 36.504481 |
| 2 | 3 | 45.148505 |
| 3 | 4 | 74.202823 |
| 4 | 5 | 74.205055 |
| 5 | 6 | 254.660597 |
| 6 | 7 | 129.066478 |
| 7 | 8 | 111.027489 |
| 8 | 9 | 171.782841 |
| 9 | 10 | 151.978352 |
| 10 | 11 | 54.424475 |
| 11 | 12 | 118.038638 |
| 12 | 13 | 51.905636 |
| 13 | 14 | 92.712426 |
| 14 | 15 | 80.325170 |
| 15 | 16 | 95.623066 |
| 16 | 17 | 73.062571 |
| 17 | 18 | 134.915158 |
| 18 | 19 | 73.366731 |
| 19 | 20 | 158.156244 |
| 20 | 21 | 59.592857 |
| 21 | 22 | 55.823892 |
| 22 | 23 | 92.098260 |
| 23 | 24 | 189.934931 |
| 24 | 25 | 65.688042 |
| 25 | 26 | 31.956221 |
| 26 | 27 | 34.876088 |
| 27 | 28 | 17.399788 |
| 28 | 29 | 61.776259 |
| 29 | 30 | 81.290729 |
| 30 | 31 | 57.090485 |
| 31 | 32 | 204.707566 |
In [63]:
dist_table.iloc[0]
Out[63]:
school_dis 16 shape_area 4.67636e+07 shape_leng 35848.9 geometry_object POLYGON ((-73.93311862859143 40.69579115384632... geometry_layer geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b geometry_proj4 +proj=longlat +ellps=WGS84 +no_defs Name: 0, dtype: object
In [64]:
mean_table.iloc[0]
Out[64]:
district 1.000000 diversity_index 74.392314 Name: 0, dtype: float64
In [65]:
t = pd.merge(dist_table, mean_table, left_on='school_dis', right_on='district')
In [66]:
map_table = t.copy()
In [67]:
map_table
Out[67]:
<style scoped>
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</style>
| school_dis | shape_area | shape_leng | geometry_object | geometry_layer | geometry_proj4 | district | diversity_index | |
|---|---|---|---|---|---|---|---|---|
| 0 | 16.0 | 4.676362e+07 | 35848.904605 | POLYGON ((-73.93311862859143 40.69579115384632... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 16 | 95.623066 |
| 1 | 32.0 | 5.189850e+07 | 37251.057847 | POLYGON ((-73.91180710069435 40.70343495202662... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 32 | 204.707566 |
| 2 | 6.0 | 9.634170e+07 | 70447.849084 | POLYGON ((-73.92640556921116 40.87762147653734... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 6 | 254.660597 |
| 3 | 31.0 | 1.604472e+09 | 434471.412859 | (POLYGON ((-74.05050806403247 40.5664220341608... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 31 | 57.090485 |
| 4 | 7.0 | 9.226247e+07 | 65294.452403 | (POLYGON ((-73.89680883223774 40.7958084451597... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 7 | 129.066478 |
| 5 | 23.0 | 4.740069e+07 | 40317.452033 | (POLYGON ((-73.92044366203014 40.6656262871675... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 23 | 92.098260 |
| 6 | 29.0 | 4.201981e+08 | 135035.241651 | POLYGON ((-73.73816144093141 40.72895809117297... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 29 | 61.776259 |
| 7 | 26.0 | 4.247909e+08 | 125677.678898 | POLYGON ((-73.74344992332192 40.77824115291502... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 26 | 31.956221 |
| 8 | 15.0 | 1.961534e+08 | 153439.165680 | POLYGON ((-73.98633135042395 40.69105051012824... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 15 | 80.325170 |
| 9 | 17.0 | 1.284414e+08 | 68341.398899 | POLYGON ((-73.92044366203014 40.6656262871675,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 17 | 73.062571 |
| 10 | 19.0 | 2.034175e+08 | 184183.167312 | (POLYGON ((-73.846736514711 40.60485301485166,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 19 | 73.366731 |
| 11 | 13.0 | 1.048706e+08 | 86635.210559 | POLYGON ((-73.97906084911834 40.70594602894087... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 13 | 51.905636 |
| 12 | 18.0 | 1.751488e+08 | 121184.158477 | (POLYGON ((-73.86706149472118 40.5820879767934... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 18 | 134.915158 |
| 13 | 20.0 | 2.426965e+08 | 94309.778946 | POLYGON ((-74.02552971543656 40.65147855069281... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 20 | 158.156244 |
| 14 | 3.0 | 1.134889e+08 | 52072.051321 | POLYGON ((-73.95671863064405 40.78660079332199... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 3 | 45.148505 |
| 15 | 5.0 | 5.251977e+07 | 44469.588221 | POLYGON ((-73.93515659239551 40.83268240623763... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 5 | 74.205055 |
| 16 | 9.0 | 8.341539e+07 | 46648.958586 | POLYGON ((-73.9212971968614 40.85428933985649,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 9 | 171.782841 |
| 17 | 1.0 | 3.516075e+07 | 28641.276279 | POLYGON ((-73.97177410965313 40.72582128133706... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 1 | 74.392314 |
| 18 | 14.0 | 1.503102e+08 | 95792.082090 | POLYGON ((-73.95439555417087 40.73911477252251... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 14 | 92.712426 |
| 19 | 4.0 | 5.262043e+07 | 52061.828459 | (POLYGON ((-73.92133752419399 40.8008521064970... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 4 | 74.202823 |
| 20 | 10.0 | 2.825415e+08 | 94957.570434 | POLYGON ((-73.86789798628736 40.90294017690526... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 10 | 151.978352 |
| 21 | 10.0 | 3.282963e+06 | 7883.372664 | (POLYGON ((-73.9089323517538 40.8721573479701,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 10 | 151.978352 |
| 22 | 12.0 | 6.907182e+07 | 48527.595776 | POLYGON ((-73.88284445574813 40.84781722645163... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 12 | 118.038638 |
| 23 | 25.0 | 4.436285e+08 | 175827.007127 | POLYGON ((-73.82049919995312 40.80101146781899... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 25 | 65.688042 |
| 24 | 28.0 | 2.475679e+08 | 114694.912786 | POLYGON ((-73.84485477879177 40.7357514698091,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 28 | 17.399788 |
| 25 | 24.0 | 3.949782e+08 | 127343.703736 | (POLYGON ((-73.90641585511733 40.7398683641967... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 24 | 189.934931 |
| 26 | 30.0 | 3.181290e+08 | 150392.978241 | POLYGON ((-73.90647314610101 40.79018117520807... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 30 | 81.290729 |
| 27 | 21.0 | 2.101971e+08 | 123858.087345 | POLYGON ((-73.96184657346174 40.62757081710622... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 21 | 59.592857 |
| 28 | 22.0 | 3.855533e+08 | 271718.504936 | (POLYGON ((-73.91990064270161 40.5996005215871... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 22 | 55.823892 |
| 29 | 27.0 | 7.955970e+08 | 589135.490708 | (POLYGON ((-73.82784008953526 40.5887858248046... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 27 | 34.876088 |
| 30 | 11.0 | 3.926651e+08 | 305305.869806 | (POLYGON ((-73.78833349834532 40.8346671297593... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 11 | 54.424475 |
| 31 | 8.0 | 2.588266e+08 | 223080.044096 | (POLYGON ((-73.83979488562292 40.8356192069902... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 8 | 111.027489 |
| 32 | 2.0 | 2.804004e+08 | 212406.819436 | (POLYGON ((-74.0438776163991 40.69018767537123... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 2 | 36.504481 |
In [68]:
map_table['FillBlues'] = map_table[selected_attribute]
In [69]:
school_map_table = school_table.copy()
In [70]:
school_map_table['geometry_object'] = school_map_table.WKT.apply(wkt.loads)
In [71]:
district_map_table = t.copy()
district_map_table['FillBlues'] = district_map_table[selected_attribute]
school_map_table = school_table.copy()
#school_map_table['FillBlues'] = district_map_table['economic_need_index'].max()
map_table = pd.concat([
#district_map_table,
school_map_table,
], sort=False)
In [72]:
url = 'https://data.cityofnewyork.us/api/views/vh2h-md7a/rows.csv'
#school_graduation_table = pd.read_csv(url)
#len(school_graduation_table)
In [73]:
#school_graduation_table.iloc[0]
In [74]:
#school_graduation_table.groupby('DBN').mean()[:5]
In [75]:
#school_graduation_table = school_graduation_table[['DBN', 'School Name', 'Total Grads - % of cohort']].copy()
#len(school_graduation_table)
In [76]:
#school_graduation_table = school_graduation_table.dropna()
#sum(school_graduation_table['Total Grads - % of cohort'] == 's')
In [77]:
school_graduation_table = pd.read_csv(url, na_values=['s'])
In [78]:
school_graduation_table.loc[0]
Out[78]:
Demographic Total Cohort DBN 01M292 School Name HENRY STREET SCHOOL FOR INTERNATIONAL Cohort 2003 Total Cohort 5 Total Grads - n NaN Total Grads - % of cohort NaN Total Regents - n NaN Total Regents - % of cohort NaN Total Regents - % of grads NaN Advanced Regents - n NaN Advanced Regents - % of cohort NaN Advanced Regents - % of grads NaN Regents w/o Advanced - n NaN Regents w/o Advanced - % of cohort NaN Regents w/o Advanced - % of grads NaN Local - n NaN Local - % of cohort NaN Local - % of grads NaN Still Enrolled - n NaN Still Enrolled - % of cohort NaN Dropped Out - n NaN Dropped Out - % of cohort NaN Name: 0, dtype: object
In [79]:
# Define which values mean null
school_graduation_table = school_graduation_table[['DBN', 'School Name', selected_outcome]].copy()
school_graduation_table = school_graduation_table.dropna()
len(school_graduation_table)
Out[79]:
16704
In [80]:
school_graduation_table.dtypes
Out[80]:
DBN object School Name object Total Grads - % of cohort float64 dtype: object
In [81]:
school_graduation_table
Out[81]:
<style scoped>
.dataframe tbody tr th:only-of-type {
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<p>16704 rows × 3 columns</p>
| DBN | School Name | Total Grads - % of cohort | |
|---|---|---|---|
| 1 | 01M292 | HENRY STREET SCHOOL FOR INTERNATIONAL | 67.3 |
| 2 | 01M292 | HENRY STREET SCHOOL FOR INTERNATIONAL | 67.2 |
| 3 | 01M292 | HENRY STREET SCHOOL FOR INTERNATIONAL | 55.1 |
| 4 | 01M292 | HENRY STREET SCHOOL FOR INTERNATIONAL | 56.4 |
| 5 | 01M448 | UNIVERSITY NEIGHBORHOOD HIGH SCHOOL | 71.9 |
| 6 | 01M448 | UNIVERSITY NEIGHBORHOOD HIGH SCHOOL | 63.5 |
| 7 | 01M448 | UNIVERSITY NEIGHBORHOOD HIGH SCHOOL | 77.0 |
| 8 | 01M448 | UNIVERSITY NEIGHBORHOOD HIGH SCHOOL | 67.0 |
| 9 | 01M448 | UNIVERSITY NEIGHBORHOOD HIGH SCHOOL | 52.9 |
| 10 | 01M448 | UNIVERSITY NEIGHBORHOOD HIGH SCHOOL | 42.7 |
| 11 | 01M448 | UNIVERSITY NEIGHBORHOOD HIGH SCHOOL | 48.4 |
| 12 | 01M450 | EAST SIDE COMMUNITY SCHOOL | 67.2 |
| 13 | 01M450 | EAST SIDE COMMUNITY SCHOOL | 62.5 |
| 14 | 01M450 | EAST SIDE COMMUNITY SCHOOL | 62.0 |
| 15 | 01M450 | EAST SIDE COMMUNITY SCHOOL | 72.5 |
| 16 | 01M450 | EAST SIDE COMMUNITY SCHOOL | 71.8 |
| 17 | 01M450 | EAST SIDE COMMUNITY SCHOOL | 77.8 |
| 18 | 01M450 | EAST SIDE COMMUNITY SCHOOL | 78.9 |
| 19 | 01M509 | MARTA VALLE HIGH SCHOOL | 49.3 |
| 20 | 01M509 | MARTA VALLE HIGH SCHOOL | 40.0 |
| 21 | 01M509 | MARTA VALLE HIGH SCHOOL | 44.3 |
| 22 | 01M509 | MARTA VALLE HIGH SCHOOL | 44.6 |
| 23 | 01M509 | MARTA VALLE HIGH SCHOOL | 55.7 |
| 24 | 01M509 | MARTA VALLE HIGH SCHOOL | 56.0 |
| 25 | 01M509 | MARTA VALLE HIGH SCHOOL | 59.5 |
| 26 | 01M515 | LOWER EAST SIDE PREPARATORY HIGH SCHO | 53.0 |
| 27 | 01M515 | LOWER EAST SIDE PREPARATORY HIGH SCHO | 44.8 |
| 28 | 01M515 | LOWER EAST SIDE PREPARATORY HIGH SCHO | 42.0 |
| 29 | 01M515 | LOWER EAST SIDE PREPARATORY HIGH SCHO | 48.5 |
| 30 | 01M515 | LOWER EAST SIDE PREPARATORY HIGH SCHO | 45.5 |
| ... | ... | ... | ... |
| 25060 | 32K545 | EBC HIGH SCHOOL FOR PUBLIC SERVICE?BU | 58.6 |
| 25061 | 32K545 | EBC HIGH SCHOOL FOR PUBLIC SERVICE?BU | 50.0 |
| 25062 | 32K545 | EBC HIGH SCHOOL FOR PUBLIC SERVICE?BU | 60.3 |
| 25063 | 32K545 | EBC HIGH SCHOOL FOR PUBLIC SERVICE?BU | 56.2 |
| 25064 | 32K545 | EBC HIGH SCHOOL FOR PUBLIC SERVICE?BU | 63.0 |
| 25066 | 32K549 | BUSHWICK SCHOOL FOR SOCIAL JUSTICE | 57.1 |
| 25067 | 32K549 | BUSHWICK SCHOOL FOR SOCIAL JUSTICE | 70.4 |
| 25068 | 32K549 | BUSHWICK SCHOOL FOR SOCIAL JUSTICE | 69.4 |
| 25069 | 32K549 | BUSHWICK SCHOOL FOR SOCIAL JUSTICE | 71.1 |
| 25070 | 32K549 | BUSHWICK SCHOOL FOR SOCIAL JUSTICE | 71.1 |
| 25072 | 32K552 | ACADEMY OF URBAN PLANNING | 49.2 |
| 25073 | 32K552 | ACADEMY OF URBAN PLANNING | 53.2 |
| 25074 | 32K552 | ACADEMY OF URBAN PLANNING | 52.8 |
| 25075 | 32K552 | ACADEMY OF URBAN PLANNING | 42.0 |
| 25076 | 32K552 | ACADEMY OF URBAN PLANNING | 54.0 |
| 25080 | 32K554 | ALL CITY LEADERSHIP SECONDARY SCHOOL | 69.6 |
| 25081 | 32K554 | ALL CITY LEADERSHIP SECONDARY SCHOOL | 87.5 |
| 25082 | 32K554 | ALL CITY LEADERSHIP SECONDARY SCHOOL | 91.7 |
| 25084 | 32K556 | BUSHWICK LEADERS HIGH SCHOOL FOR ACAD | 63.8 |
| 25085 | 32K556 | BUSHWICK LEADERS HIGH SCHOOL FOR ACAD | 64.7 |
| 25086 | 32K556 | BUSHWICK LEADERS HIGH SCHOOL FOR ACAD | 62.5 |
| 25087 | 32K556 | BUSHWICK LEADERS HIGH SCHOOL FOR ACAD | 49.0 |
| 25088 | 32K556 | BUSHWICK LEADERS HIGH SCHOOL FOR ACAD | 51.0 |
| 25089 | 32K564 | BUSHWICK COMMUNITY HIGH SCHOOL | 2.9 |
| 25090 | 32K564 | BUSHWICK COMMUNITY HIGH SCHOOL | 5.5 |
| 25091 | 32K564 | BUSHWICK COMMUNITY HIGH SCHOOL | 1.5 |
| 25092 | 32K564 | BUSHWICK COMMUNITY HIGH SCHOOL | 3.1 |
| 25093 | 32K564 | BUSHWICK COMMUNITY HIGH SCHOOL | 3.8 |
| 25094 | 32K564 | BUSHWICK COMMUNITY HIGH SCHOOL | 7.0 |
| 25095 | 32K564 | BUSHWICK COMMUNITY HIGH SCHOOL | 7.0 |
In [82]:
school_graduation_table = school_graduation_table.groupby('DBN').mean()
school_graduation_table[:5]
Out[82]:
<style scoped>
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</style>
| Total Grads - % of cohort | |
|---|---|
| DBN | |
| 01M292 | 61.755556 |
| 01M448 | 58.637736 |
| 01M450 | 70.462500 |
| 01M509 | 49.902381 |
| 01M515 | 49.151064 |
In [83]:
endpoint_url = 'https://data.cityofnewyork.us/resource/r2nx-nhxe.csv'
In [84]:
# Load schools
school_location_table = load(
endpoint_url,
buffer_size=1000)
school_location_table[:5]
https://data.cityofnewyork.us/resource/r2nx-nhxe.csv?$limit=1000 1000 1823
Out[84]:
<style scoped>
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vertical-align: middle;
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vertical-align: top;
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</style>
<p>5 rows × 48 columns</p>
| :@computed_region_92fq_4b7q | :@computed_region_efsh_h5xi | :@computed_region_f5dn_yrer | :@computed_region_sbqj_enih | :@computed_region_yeji_bk3q | admin_district_location_code | administrative_district_name | ats_system_code | beds_number | borough_block_lot | ... | primary_building_code | principal_name | principal_phone_number | principal_title | school_support_team_leader_name | school_support_team_name | state_code | status_descriptions | x_coordinate | y_coordinate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 50.0 | 11729.0 | 70.0 | 5.0 | 4.0 | M801 | COMMUNITY SCHOOL DISTRICT 01 | 01M015 | 310100010015 | 1003740020 | ... | M015 | IRENE SANCHEZ | 212-228-8730 | PRINCIPAL | NaN | School Support Team 3- Manhattan | NY | Open | 990141.0 | 202349.0 |
| 1 | 50.0 | 11724.0 | 70.0 | 5.0 | 4.0 | M801 | COMMUNITY SCHOOL DISTRICT 01 | 01M019 | 310100010019 | 1004530034 | ... | M019 | JACQUELINE FLANAGAN | 212-533-5340 | PRINCIPAL | NaN | School Support Team 3- Manhattan | NY | Open | 988547.0 | 205239.0 |
| 2 | 32.0 | 11723.0 | 70.0 | 4.0 | 4.0 | M801 | COMMUNITY SCHOOL DISTRICT 01 | 01M020 | 310100010020 | 1003550001 | ... | M020 | SARAH PINTO VIAGRAN | 212-254-9577 | PRINCIPAL | NaN | School Support Team 3- Manhattan | NY | Open | 988044.0 | 202068.0 |
| 3 | 50.0 | 11729.0 | 70.0 | 5.0 | 4.0 | M801 | COMMUNITY SCHOOL DISTRICT 01 | 01M034 | 310100010034 | 1003810038 | ... | M034 | ANGELIKI LOUKATOS | 212-228-4433 | PRINCIPAL | NaN | School Support Team 3- Manhattan | NY | Open | 991163.0 | 203782.0 |
| 4 | 50.0 | 11729.0 | 70.0 | 5.0 | 4.0 | M801 | COMMUNITY SCHOOL DISTRICT 01 | 01M063 | 310100010063 | 1004310014 | ... | M063 | DARLENE CAMERON | 212-674-3180 | PRINCIPAL | NaN | School Support Team 3- Manhattan | NY | Open | 988071.0 | 203210.0 |
In [85]:
school_location_table.iloc[0]
Out[85]:
:@computed_region_92fq_4b7q 50 :@computed_region_efsh_h5xi 11729 :@computed_region_f5dn_yrer 70 :@computed_region_sbqj_enih 5 :@computed_region_yeji_bk3q 4 admin_district_location_code M801 administrative_district_name COMMUNITY SCHOOL DISTRICT 01 ats_system_code 01M015 beds_number 310100010015 borough_block_lot 1003740020 census_tract 2601 community_district 103 community_school_sup_name PHILLIPS, DANIELLA council_district 2 fax_number 212-477-0931 field_support_center_leader_name CHU, YUET field_support_center_name Field Support Center - Manhattan fiscal_year 2018 geographical_district_code 1 grades_final_text PK,0K,01,02,03,04,05 grades_text PK,0K,01,02,03,04,05,SE highschool_network_location_code NaN highschool_network_name NaN highschool_network_superintendent NaN location_1 POINT (-73.978747 40.722075) location_1_address 333 EAST 4 STREET location_1_city MANHATTAN location_1_state NY location_1_zip 10009 location_category_description Elementary location_code M015 location_name P.S. 015 Roberto Clemente location_type_description General Academic managed_by_name DOE nta MN28 nta_name Lower East Side ... open_date 1904-07-01T00:00:00.000 primary_address_line_1 333 EAST 4 STREET primary_building_code M015 principal_name IRENE SANCHEZ principal_phone_number 212-228-8730 principal_title PRINCIPAL school_support_team_leader_name NaN school_support_team_name School Support Team 3- Manhattan state_code NY status_descriptions Open x_coordinate 990141 y_coordinate 202349 Name: 0, dtype: object
In [86]:
school_location_table.iloc[0]['location_1']
Out[86]:
'POINT (-73.978747 40.722075)'
In [87]:
school_location_table.iloc[0]['ats_system_code']
Out[87]:
'01M015 '
In [88]:
school_location_table['DBN'] = school_location_table['ats_system_code'].str.strip()
In [89]:
school_location_table.iloc[0]['DBN']
Out[89]:
'01M015'
In [90]:
school_location_table = school_location_table.rename(columns={
'location_1': 'WKT',
'location_name': 'School Name',
})
In [91]:
trimmed_school_location_table = school_location_table[[
'DBN',
'WKT',
'School Name',
]]
In [92]:
merged_school_table = pd.merge(
school_table,
school_graduation_table,
left_on='DBN',
right_on='DBN')
len(school_table)
Out[92]:
1801
In [93]:
merged_school_table.iloc[0]
Out[93]:
DBN 01M292 WKT POINT (-73.986051 40.713362) School Name Orchard Collegiate Academy asian_1 21 asian_2 15 black_1 34 black_2 24.3 dbn 01M292 economic_need_index 84.4% english_language_learners_1 20 english_language_learners_2 14.3 female_1 53 female_2 37.9 grade_1 0 grade_10 35 grade_11 31 grade_12 34 grade_2 0 grade_3 0 grade_4 0 grade_5 0 grade_6 0 grade_7 0 grade_8 0 grade_9 40 grade_k 0 grade_pk_half_day_full_day 0 hispanic_1 77 hispanic_2 55 male_1 87 male_2 62.1 multiple_race_categories_not_represented_1 1 multiple_race_categories_not_represented_2 0.7 poverty_1 128 poverty_2 91.4 school_name Orchard Collegiate Academy students_with_disabilities_1 38 students_with_disabilities_2 27.1 total_enrollment 140 white_1 7 white_2 5 year 2016-17 diversity_index 86.2143 scaled_index 0.00561885 district 1 Total Grads - % of cohort 61.7556 Name: 0, dtype: object
In [94]:
sorted_school_table = merged_school_table.sort_values(selected_outcome, ascending=False)
In [95]:
sorted_school_table[-10:]
Out[95]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<p>10 rows × 46 columns</p>
| DBN | WKT | School Name | asian_1 | asian_2 | black_1 | black_2 | dbn | economic_need_index | english_language_learners_1 | ... | students_with_disabilities_1 | students_with_disabilities_2 | total_enrollment | white_1 | white_2 | year | diversity_index | scaled_index | district | Total Grads - % of cohort | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 89 | 07X379 | POINT (-73.905389 40.818703) | Jill Chaifetz Transfer High School | 3 | 1.4 | 74 | 34.3 | 07X379 | 80.6% | 21 | ... | 56 | 25.9 | 216 | 1 | 0.5 | 2016-17 | 67.785714 | 0.009317 | 7 | 12.929167 |
| 104 | 08X377 | POINT (-73.85593 40.821218) | Bronx Community High School | 5 | 2.8 | 62 | 34.4 | 08X377 | 75.1% | 6 | ... | 30 | 16.7 | 180 | 3 | 1.7 | 2016-17 | 81.727273 | NaN | 8 | 10.683333 |
| 252 | 23K646 | POINT (-73.904143 40.677528) | Aspirations Diploma Plus High School | 3 | 1.3 | 178 | 75.4 | 23K646 | 69.0% | 7 | ... | 43 | 18.2 | 236 | 1 | 0.4 | 2016-17 | 48.500000 | NaN | 23 | 10.335000 |
| 90 | 07X381 | POINT (-73.919949 40.818645) | Bronx Haven High School | 3 | 1.6 | 62 | 32.3 | 07X381 | 78.8% | 10 | ... | 46 | 24.0 | 192 | 1 | 0.5 | 2016-17 | 95.800000 | 0.015825 | 7 | 9.500000 |
| 184 | 24Q744 | POINT (-73.871505 40.743228) | VOYAGES Preparatory | 23 | 9.2 | 47 | 18.7 | 24Q744 | 48.7% | 3 | ... | 15 | 6.0 | 251 | 12 | 4.8 | 2016-17 | 210.000000 | NaN | 24 | 9.200000 |
| 220 | 17K568 | POINT (-73.923881 40.666251) | Brownsville Academy High School | 1 | 0.6 | 133 | 85.8 | 17K568 | 67.0% | 3 | ... | 36 | 23.2 | 155 | 0 | 0.0 | 2016-17 | 31.679487 | NaN | 17 | 8.907143 |
| 76 | 05M285 | POINT (-73.939974 40.807692) | Harlem Renaissance High School | 7 | 3.0 | 99 | 42.9 | 05M285 | 77.4% | 31 | ... | 52 | 22.5 | 231 | 3 | 1.3 | 2016-17 | 100.555556 | 0.043929 | 5 | 7.521429 |
| 224 | 18K673 | POINT (-73.920658 40.659914) | East Brooklyn Community High School | 0 | 0.0 | 162 | 83.9 | 18K673 | 67.2% | 12 | ... | 67 | 34.7 | 193 | 4 | 2.1 | 2016-17 | 118.300000 | NaN | 18 | 5.441667 |
| 322 | 32K564 | POINT (-73.915217 40.695875) | Bushwick Community High School | 0 | 0.0 | 67 | 28.4 | 32K564 | 71.0% | 10 | ... | 56 | 23.7 | 236 | 7 | 3.0 | 2016-17 | 234.000000 | NaN | 32 | 5.337500 |
| 182 | 13K616 | POINT (-73.95818 40.692015) | Brooklyn High School for Leadership and Community | 1 | 0.5 | 141 | 69.5 | 13K616 | 70.6% | 9 | ... | 38 | 18.7 | 203 | 5 | 2.5 | 2016-17 | 187.200000 | NaN | 13 | 5.322222 |
In [96]:
sorted_school_table[-3:]
Out[96]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<p>3 rows × 46 columns</p>
| DBN | WKT | School Name | asian_1 | asian_2 | black_1 | black_2 | dbn | economic_need_index | english_language_learners_1 | ... | students_with_disabilities_1 | students_with_disabilities_2 | total_enrollment | white_1 | white_2 | year | diversity_index | scaled_index | district | Total Grads - % of cohort | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 224 | 18K673 | POINT (-73.920658 40.659914) | East Brooklyn Community High School | 0 | 0.0 | 162 | 83.9 | 18K673 | 67.2% | 12 | ... | 67 | 34.7 | 193 | 4 | 2.1 | 2016-17 | 118.3 | NaN | 18 | 5.441667 |
| 322 | 32K564 | POINT (-73.915217 40.695875) | Bushwick Community High School | 0 | 0.0 | 67 | 28.4 | 32K564 | 71.0% | 10 | ... | 56 | 23.7 | 236 | 7 | 3.0 | 2016-17 | 234.0 | NaN | 32 | 5.337500 |
| 182 | 13K616 | POINT (-73.95818 40.692015) | Brooklyn High School for Leadership and Community | 1 | 0.5 | 141 | 69.5 | 13K616 | 70.6% | 9 | ... | 38 | 18.7 | 203 | 5 | 2.5 | 2016-17 | 187.2 | NaN | 13 | 5.322222 |
In [97]:
school_table.iloc[0]
Out[97]:
DBN 01M015 WKT POINT (-73.978747 40.722075) School Name P.S. 015 Roberto Clemente asian_1 14 asian_2 7.9 black_1 51 black_2 28.7 dbn 01M015 economic_need_index 89.2% english_language_learners_1 12 english_language_learners_2 6.7 female_1 83 female_2 46.6 grade_1 33 grade_10 0 grade_11 0 grade_12 0 grade_2 27 grade_3 31 grade_4 24 grade_5 18 grade_6 0 grade_7 0 grade_8 0 grade_9 0 grade_k 28 grade_pk_half_day_full_day 17 hispanic_1 105 hispanic_2 59 male_1 95 male_2 53.4 multiple_race_categories_not_represented_1 4 multiple_race_categories_not_represented_2 2.2 poverty_1 152 poverty_2 85.4 school_name P.S. 015 Roberto Clemente students_with_disabilities_1 51 students_with_disabilities_2 28.7 total_enrollment 178 white_1 4 white_2 2.2 year 2016-17 diversity_index 35.2727 scaled_index 0.096328 district 1 Name: 0, dtype: object
In [98]:
len(district_map_table)
Out[98]:
33
In [99]:
district_map_table = t.copy()
district_map_table['WKT'] = district_map_table['geometry_object'].apply(
lambda x: x.wkt)
# Define FillColor
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(1, 100))
array = district_map_table['diversity_index'].values.reshape(-1, 1)
scaler.fit(array)
district_map_table['scaled_index'] = scaler.transform(array)
In [100]:
from sklearn import preprocessing
import pandas as pd
x = sorted_school_table['diversity_index'].values.reshape(-1,1) #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
sorted_school_table['scaled_index'] = pd.DataFrame(x_scaled)
# x = district_map_table['diversity_index'].values.reshape(-1,1) #returns a numpy array
# min_max_scaler = preprocessing.MinMaxScaler()
# x_scaled = min_max_scaler.fit_transform(x)
# district_map_table['scaled_index'] = pd.DataFrame(x_scaled)
In [101]:
sorted_school_table.scaled_index.describe()
Out[101]:
count 323.000000 mean 0.081404 std 0.100232 min 0.000000 25% 0.026000 50% 0.054133 75% 0.096842 max 1.000000 Name: scaled_index, dtype: float64
In [102]:
# getting top and bottom performing schools
a_school_map_table = sorted_school_table[:10].copy()
a_school_map_table = a_school_map_table[['WKT', 'School Name','total_enrollment']]
a_school_map_table = a_school_map_table.rename({
'School Name': 'Name',
})
a_school_map_table['FillColor'] = 'g'
In [103]:
len(sorted_school_table)
Out[103]:
323
In [104]:
sorted_school_table.drop(sorted_school_table.tail(1).index,inplace=True)
In [105]:
sorted_school_table.isna().sum()
Out[105]:
DBN 0 WKT 0 School Name 0 asian_1 0 asian_2 0 black_1 0 black_2 0 dbn 0 economic_need_index 0 english_language_learners_1 0 english_language_learners_2 0 female_1 0 female_2 0 grade_1 0 grade_10 0 grade_11 0 grade_12 0 grade_2 0 grade_3 0 grade_4 0 grade_5 0 grade_6 0 grade_7 0 grade_8 0 grade_9 0 grade_k 0 grade_pk_half_day_full_day 0 hispanic_1 0 hispanic_2 0 male_1 0 male_2 0 multiple_race_categories_not_represented_1 0 multiple_race_categories_not_represented_2 0 poverty_1 0 poverty_2 0 school_name 0 students_with_disabilities_1 0 students_with_disabilities_2 0 total_enrollment 0 white_1 0 white_2 0 year 0 diversity_index 0 scaled_index 0 district 0 Total Grads - % of cohort 0 dtype: int64
In [106]:
sorted_school_table['School Name'].nunique()
Out[106]:
322
In [107]:
sorted_school_table[30:]
Out[107]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<p>292 rows × 46 columns</p>
| DBN | WKT | School Name | asian_1 | asian_2 | black_1 | black_2 | dbn | economic_need_index | english_language_learners_1 | ... | students_with_disabilities_1 | students_with_disabilities_2 | total_enrollment | white_1 | white_2 | year | diversity_index | scaled_index | district | Total Grads - % of cohort | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | 13K430 | POINT (-73.976435 40.688896) | Brooklyn Technical High School | 3483 | 61.3 | 391 | 6.9 | 13K430 | 41.8% | 0 | ... | 94 | 1.7 | 5682 | 1243 | 21.9 | 2016-17 | 27.388889 | 0.017592 | 13 | 91.419048 |
| 32 | 02M439 | POINT (-73.991966 40.741963) | Manhattan Village Academy | 31 | 7.2 | 49 | 11.3 | 02M439 | 63.0% | 7 | ... | 65 | 15.0 | 432 | 34 | 7.9 | 2016-17 | 5.492806 | 0.013822 | 2 | 91.339130 |
| 248 | 22K555 | POINT (-73.972312 40.649055) | Brooklyn College Academy | 51 | 8.3 | 422 | 68.3 | 22K555 | 45.9% | 1 | ... | 58 | 9.4 | 618 | 37 | 6.0 | 2016-17 | 13.621212 | 0.043673 | 15 | 91.128571 |
| 63 | 03M479 | POINT (-73.996262 40.760858) | Beacon High School | 105 | 8.1 | 177 | 13.6 | 03M479 | 26.3% | 3 | ... | 103 | 7.9 | 1304 | 670 | 51.4 | 2016-17 | 7.922535 | 0.008051 | 2 | 90.550877 |
| 285 | 27Q650 | POINT (-73.841475 40.689177) | High School for Construction Trades, Engineeri... | 320 | 31.5 | 98 | 9.7 | 27Q650 | 42.8% | 15 | ... | 142 | 14.0 | 1015 | 135 | 13.3 | 2016-17 | 3.736842 | 0.047347 | 27 | 90.462500 |
| 255 | 24Q264 | POINT (-73.937004 40.744175) | Academy of Finance and Enterprise | 129 | 22.8 | 31 | 5.5 | 24Q264 | 55.1% | 35 | ... | 70 | 12.3 | 567 | 93 | 16.4 | 2016-17 | 21.807692 | 0.050496 | 24 | 89.683333 |
| 268 | 25Q285 | POINT (-73.789365 40.764884) | World Journalism Preparatory: A College Board ... | 94 | 16.6 | 25 | 4.4 | 25Q285 | 31.7% | 3 | ... | 118 | 20.8 | 566 | 264 | 46.6 | 2016-17 | 63.333333 | 0.426327 | 25 | 89.025000 |
| 143 | 10X477 | POINT (-73.912686 40.877379) | Marble Hill High School for International Studies | 41 | 9.6 | 93 | 21.7 | 10X477 | 80.1% | 127 | ... | 49 | 11.4 | 429 | 22 | 5.1 | 2016-17 | 153.100000 | 0.037453 | 10 | 88.993023 |
| 38 | 02M519 | POINT (-73.959777 40.765638) | Talent Unlimited High School | 20 | 4.0 | 163 | 32.9 | 02M519 | 44.8% | 2 | ... | 74 | 14.9 | 495 | 78 | 15.8 | 2016-17 | 8.292308 | 0.007297 | 2 | 88.825000 |
| 26 | 02M414 | POINT (-74.002222 40.742512) | N.Y.C. Museum School | 135 | 29.7 | 59 | 13.0 | 02M414 | 55.6% | 6 | ... | 48 | 10.6 | 454 | 73 | 16.1 | 2016-17 | 8.086957 | 0.010869 | 2 | 88.683721 |
| 221 | 17K590 | POINT (-73.951823 40.66679) | Medgar Evers College Preparatory School | 35 | 2.8 | 1104 | 88.2 | 17K590 | 46.3% | 5 | ... | 80 | 6.4 | 1252 | 8 | 0.6 | 2016-17 | 36.614286 | 0.047102 | 17 | 88.546512 |
| 45 | 02M545 | POINT (-73.989329 40.717329) | High School for Dual Language and Asian Studies | 335 | 84.4 | 17 | 4.3 | 02M545 | 67.8% | 47 | ... | 11 | 2.8 | 397 | 14 | 3.5 | 2016-17 | 79.700000 | 0.026266 | 2 | 87.876471 |
| 159 | 11X542 | POINT (-73.860729 40.860443) | Pelham Preparatory Academy | 29 | 5.9 | 149 | 30.2 | 11X542 | 61.3% | 19 | ... | 103 | 20.9 | 493 | 47 | 9.5 | 2016-17 | 23.678571 | 0.047720 | 11 | 86.530952 |
| 113 | 09X260 | POINT (-73.902794 40.833884) | Bronx Center for Science and Mathematics | 32 | 7.0 | 98 | 21.4 | 09X260 | 76.8% | 27 | ... | 105 | 22.9 | 458 | 7 | 1.5 | 2016-17 | 98.722222 | 0.143469 | 9 | 86.523810 |
| 267 | 25Q281 | POINT (-73.821402 40.74943) | East-West School of International Studies | 451 | 65.8 | 67 | 9.8 | 25Q281 | 47.3% | 45 | ... | 82 | 12.0 | 685 | 34 | 5.0 | 2016-17 | 51.500000 | 0.298061 | 25 | 86.116667 |
| 175 | 13K483 | POINT (-73.988323 40.694629) | The Urban Assembly School for Law and Justice | 12 | 2.6 | 313 | 68.0 | 13K483 | 56.6% | 8 | ... | 76 | 16.5 | 460 | 7 | 1.5 | 2016-17 | 23.783784 | 0.118868 | 13 | 85.511111 |
| 247 | 22K535 | POINT (-73.934449 40.578356) | Leon M. Goldstein High School for the Sciences | 243 | 23.6 | 101 | 9.8 | 22K535 | 35.0% | 7 | ... | 163 | 15.8 | 1029 | 576 | 56.0 | 2016-17 | 52.166667 | 0.016808 | 22 | 85.468519 |
| 208 | 17K382 | POINT (-73.957173 40.649602) | Academy for College Preparation and Career Exp... | 8 | 1.9 | 323 | 78.4 | 17K382 | 69.5% | 39 | ... | 69 | 16.7 | 412 | 7 | 1.7 | 2016-17 | 24.772727 | 0.026482 | 17 | 85.358333 |
| 320 | 32K554 | POINT (-73.913172 40.697378) | All City Leadership Secondary School | 25 | 6.1 | 42 | 10.2 | 32K554 | 59.6% | 7 | ... | 33 | 8.0 | 413 | 29 | 7.0 | 2016-17 | 205.500000 | 0.142000 | 32 | 85.145161 |
| 12 | 02M298 | POINT (-73.993607 40.716231) | Pace High School | 43 | 8.5 | 162 | 31.9 | 02M298 | 66.3% | 12 | ... | 98 | 19.3 | 508 | 13 | 2.6 | 2016-17 | 16.311111 | 0.001725 | 2 | 85.086207 |
| 304 | 30Q575 | POINT (-73.937564 40.751144) | Academy of American Studies | 294 | 29.4 | 64 | 6.4 | 30Q575 | 40.4% | 40 | ... | 84 | 8.4 | 999 | 312 | 31.2 | 2016-17 | 92.250000 | 0.017824 | 30 | 84.996825 |
| 197 | 15K448 | POINT (-74.002022 40.679462) | Brooklyn Secondary School for Collaborative St... | 20 | 3.0 | 253 | 37.9 | 15K448 | 61.2% | 45 | ... | 214 | 32.1 | 667 | 71 | 10.6 | 2016-17 | 33.619048 | 0.120544 | 15 | 84.621739 |
| 151 | 11X275 | POINT (-73.861649 40.875172) | High School of Computers and Technology | 33 | 6.3 | 180 | 34.3 | 11X275 | 73.5% | 35 | ... | 136 | 25.9 | 525 | 10 | 1.9 | 2016-17 | 43.000000 | 0.036644 | 11 | 84.244000 |
| 112 | 09X252 | POINT (-73.901774 40.839508) | Mott Hall Bronx High School | 3 | 0.8 | 81 | 20.8 | 09X252 | 84.0% | 51 | ... | 95 | 24.4 | 390 | 4 | 1.0 | 2016-17 | 48.452381 | 0.027449 | 9 | 84.209524 |
| 96 | 07X548 | POINT (-73.922366 40.821828) | Careers in Sports High School | 4 | 0.7 | 168 | 30.0 | 07X548 | 83.0% | 57 | ... | 137 | 24.5 | 560 | 10 | 1.8 | 2016-17 | 95.000000 | 0.010853 | 7 | 83.679070 |
| 125 | 10X141 | POINT (-73.914141 40.887407) | Riverdale / Kingsbridge Academy (Middle School... | 126 | 8.6 | 137 | 9.4 | 10X141 | 40.0% | 113 | ... | 281 | 19.3 | 1459 | 388 | 26.6 | 2016-17 | 79.000000 | 0.092347 | 10 | 83.616667 |
| 191 | 14K488 | POINT (-73.954815 40.715561) | Brooklyn Preparatory High School | 11 | 2.1 | 264 | 50.2 | 14K488 | 64.3% | 11 | ... | 87 | 16.5 | 526 | 7 | 1.3 | 2016-17 | 53.823529 | 0.091254 | 14 | 83.595833 |
| 128 | 10X237 | POINT (-73.901565 40.875636) | The Marie Curie School for Medicine, Nursing, and | 17 | 3.2 | 175 | 32.8 | 10X237 | 76.3% | 67 | ... | 109 | 20.5 | 533 | 15 | 2.8 | 2016-17 | 35.625000 | 0.024320 | 10 | 83.466667 |
| 88 | 07X221 | POINT (-73.920851 40.813612) | South Bronx Preparatory: A College Board School | 12 | 1.8 | 153 | 23.3 | 07X221 | 86.2% | 40 | ... | 166 | 25.3 | 657 | 11 | 1.7 | 2016-17 | 86.136364 | 0.057254 | 7 | 83.400000 |
| 193 | 14K561 | POINT (-73.954815 40.715561) | Williamsburg Preparatory School | 17 | 2.5 | 116 | 16.9 | 14K561 | 66.6% | 23 | ... | 123 | 17.9 | 686 | 65 | 9.5 | 2016-17 | 130.214286 | 0.017880 | 14 | 83.339286 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 206 | 16K455 | POINT (-73.931604 40.678478) | Boys and Girls High School | 7 | 1.8 | 333 | 84.1 | 16K455 | 78.7% | 20 | ... | 115 | 29.0 | 396 | 8 | 2.0 | 2016-17 | 51.891304 | 0.078995 | 16 | 36.258621 |
| 179 | 13K575 | POINT (-73.947873 40.684654) | Bedford Stuyvesant Preparatory High School | 0 | 0.0 | 100 | 82.0 | 13K575 | 70.4% | 4 | ... | 8 | 6.6 | 122 | 1 | 0.8 | 2016-17 | 71.750000 | 0.073388 | 13 | 33.620000 |
| 132 | 10X319 | POINT (-73.893744 40.848385) | Providing Urban Learners Success In Education ... | 1 | 0.4 | 27 | 11.4 | 10X319 | 78.6% | 16 | ... | 41 | 17.4 | 236 | 1 | 0.4 | 2016-17 | 2.728956 | 0.121061 | 10 | 27.394595 |
| 61 | 03M404 | POINT (-73.974483 40.786134) | Innovation Diploma Plus | 1 | 0.6 | 61 | 35.1 | 03M404 | 75.2% | 1 | ... | 24 | 13.8 | 174 | 0 | 0.0 | 2016-17 | 57.117647 | 0.015560 | 3 | 27.300000 |
| 53 | 02M586 | POINT (-73.992343 40.729661) | Harvey Milk High School | 2 | 2.7 | 22 | 29.7 | 02M586 | 72.6% | 3 | ... | 30 | 40.5 | 74 | 6 | 8.1 | 2016-17 | 55.000000 | 0.056472 | 2 | 26.966667 |
| 313 | 31R470 | POINT (-74.087072 40.61062) | Concord High School | 3 | 1.6 | 53 | 29.1 | 31R470 | 66.6% | 2 | ... | 51 | 28.0 | 182 | 34 | 18.7 | 2016-17 | 20.962963 | 0.080146 | 31 | 25.826531 |
| 244 | 21K728 | POINT (-73.985413 40.576976) | Liberation Diploma Plus | 5 | 2.4 | 115 | 55.8 | 21K728 | 72.1% | 2 | ... | 47 | 22.8 | 206 | 23 | 11.2 | 2016-17 | 144.600000 | 0.159650 | 21 | 24.841667 |
| 253 | 23K647 | POINT (-73.906662 40.655644) | Metropolitan Diploma Plus High School | 0 | 0.0 | 179 | 84.8 | 23K647 | 74.4% | 2 | ... | 53 | 25.1 | 211 | 1 | 0.5 | 2016-17 | 50.333333 | 0.094768 | 23 | 20.283333 |
| 165 | 12X480 | POINT (-73.898265 40.823124) | Bronx Regional High School | 2 | 0.8 | 84 | 35.4 | 12X480 | 77.4% | 27 | ... | 37 | 15.6 | 237 | 5 | 2.1 | 2016-17 | 94.100000 | 0.059088 | 12 | 20.256000 |
| 251 | 23K643 | POINT (-73.906662 40.655644) | Brooklyn Democracy Academy | 2 | 1.0 | 167 | 80.3 | 23K643 | 71.9% | 8 | ... | 47 | 22.6 | 208 | 1 | 0.5 | 2016-17 | 46.541667 | 0.094286 | 23 | 19.838889 |
| 67 | 03M505 | POINT (-73.966779 40.797136) | Edward A. Reynolds West Side High School | 3 | 0.6 | 112 | 22.8 | 03M505 | 70.4% | 43 | ... | 161 | 32.7 | 492 | 18 | 3.7 | 2016-17 | 158.333333 | 0.034122 | 3 | 19.649153 |
| 272 | 25Q540 | POINT (-73.828279 40.765624) | Queens Academy High School | 42 | 9.7 | 168 | 38.6 | 25Q540 | 49.1% | 14 | ... | 79 | 18.2 | 435 | 24 | 5.5 | 2016-17 | 17.810811 | 0.067184 | 25 | 17.066667 |
| 178 | 13K553 | POINT (-73.947873 40.684654) | Brooklyn Academy High School | 0 | 0.0 | 99 | 73.3 | 13K553 | 69.5% | 0 | ... | 37 | 27.4 | 135 | 1 | 0.7 | 2016-17 | 16.889831 | 0.081429 | 13 | 15.860465 |
| 210 | 17K489 | POINT (-73.955188 40.669561) | W.E.B. Dubois Academic High School | 2 | 1.9 | 87 | 82.9 | 17K489 | 60.8% | 1 | ... | 17 | 16.2 | 105 | 1 | 1.0 | 2016-17 | 61.394737 | 0.122408 | 17 | 15.650000 |
| 50 | 02M570 | POINT (-73.990562 40.747425) | Satellite Academy High School | 2 | 0.7 | 101 | 36.1 | 02M570 | 70.4% | 15 | ... | 53 | 18.9 | 280 | 8 | 2.9 | 2016-17 | 131.571429 | 0.110102 | 2 | 15.410345 |
| 44 | 02M544 | POINT (-73.98861 40.767848) | Independence High School | 9 | 2.7 | 122 | 36.1 | 02M544 | 70.4% | 22 | ... | 44 | 13.0 | 338 | 23 | 6.8 | 2016-17 | 32.791667 | 0.060204 | 2 | 15.380000 |
| 274 | 25Q792 | POINT (-73.819664 40.720586) | North Queens Community High School | 26 | 11.9 | 90 | 41.3 | 25Q792 | 47.4% | 10 | ... | 47 | 21.6 | 218 | 19 | 8.7 | 2016-17 | 14.891892 | 0.362041 | 25 | 14.464286 |
| 222 | 18K578 | POINT (-73.916927 40.634451) | Brooklyn Bridge Academy | 1 | 0.5 | 169 | 84.5 | 18K578 | 64.0% | 8 | ... | 43 | 21.5 | 200 | 5 | 2.5 | 2016-17 | 60.000000 | 0.021287 | 18 | 14.378261 |
| 223 | 18K635 | POINT (-73.886499 40.652203) | Olympus Academy | 0 | 0.0 | 206 | 91.2 | 18K635 | 55.7% | 4 | ... | 47 | 20.8 | 226 | 3 | 1.3 | 2016-17 | 49.500000 | 0.225510 | 19 | 13.538889 |
| 205 | 15K698 | POINT (-74.014363 40.677968) | South Brooklyn Community High School | 7 | 3.5 | 61 | 30.3 | 15K698 | 73.3% | 8 | ... | 53 | 26.4 | 201 | 11 | 5.5 | 2016-17 | 19.500000 | 0.092959 | 15 | 13.478571 |
| 203 | 15K529 | POINT (-73.992151 40.642619) | West Brooklyn Community High School | 15 | 7.0 | 23 | 10.7 | 15K529 | 65.9% | 11 | ... | 72 | 33.6 | 214 | 27 | 12.6 | 2016-17 | 97.722222 | 0.117143 | 15 | 12.954839 |
| 89 | 07X379 | POINT (-73.905389 40.818703) | Jill Chaifetz Transfer High School | 3 | 1.4 | 74 | 34.3 | 07X379 | 80.6% | 21 | ... | 56 | 25.9 | 216 | 1 | 0.5 | 2016-17 | 67.785714 | 0.075510 | 7 | 12.929167 |
| 104 | 08X377 | POINT (-73.85593 40.821218) | Bronx Community High School | 5 | 2.8 | 62 | 34.4 | 08X377 | 75.1% | 6 | ... | 30 | 16.7 | 180 | 3 | 1.7 | 2016-17 | 81.727273 | 0.023994 | 8 | 10.683333 |
| 252 | 23K646 | POINT (-73.904143 40.677528) | Aspirations Diploma Plus High School | 3 | 1.3 | 178 | 75.4 | 23K646 | 69.0% | 7 | ... | 43 | 18.2 | 236 | 1 | 0.4 | 2016-17 | 48.500000 | 0.029967 | 23 | 10.335000 |
| 90 | 07X381 | POINT (-73.919949 40.818645) | Bronx Haven High School | 3 | 1.6 | 62 | 32.3 | 07X381 | 78.8% | 10 | ... | 46 | 24.0 | 192 | 1 | 0.5 | 2016-17 | 95.800000 | 0.041985 | 7 | 9.500000 |
| 184 | 24Q744 | POINT (-73.871505 40.743228) | VOYAGES Preparatory | 23 | 9.2 | 47 | 18.7 | 24Q744 | 48.7% | 3 | ... | 15 | 6.0 | 251 | 12 | 4.8 | 2016-17 | 210.000000 | 0.035667 | 24 | 9.200000 |
| 220 | 17K568 | POINT (-73.923881 40.666251) | Brownsville Academy High School | 1 | 0.6 | 133 | 85.8 | 17K568 | 67.0% | 3 | ... | 36 | 23.2 | 155 | 0 | 0.0 | 2016-17 | 31.679487 | 0.372449 | 17 | 8.907143 |
| 76 | 05M285 | POINT (-73.939974 40.807692) | Harlem Renaissance High School | 7 | 3.0 | 99 | 42.9 | 05M285 | 77.4% | 31 | ... | 52 | 22.5 | 231 | 3 | 1.3 | 2016-17 | 100.555556 | 0.055118 | 5 | 7.521429 |
| 224 | 18K673 | POINT (-73.920658 40.659914) | East Brooklyn Community High School | 0 | 0.0 | 162 | 83.9 | 18K673 | 67.2% | 12 | ... | 67 | 34.7 | 193 | 4 | 2.1 | 2016-17 | 118.300000 | 0.030082 | 18 | 5.441667 |
| 322 | 32K564 | POINT (-73.915217 40.695875) | Bushwick Community High School | 0 | 0.0 | 67 | 28.4 | 32K564 | 71.0% | 10 | ... | 56 | 23.7 | 236 | 7 | 3.0 | 2016-17 | 234.000000 | 0.226367 | 32 | 5.337500 |
In [108]:
len(sorted_school_table.diversity_index)
Out[108]:
322
In [109]:
#max(sorted_school_table.diversity_index)
In [110]:
b_school_map_table = sorted_school_table[-10:].copy()
b_school_map_table = b_school_map_table[['WKT', 'School Name','total_enrollment']]
b_school_map_table = b_school_map_table.rename({
'School Name': 'Name',
})
b_school_map_table['FillColor'] = 'r'
In [111]:
t = pd.merge(dist_table, mean_table, left_on='school_dis', right_on='district')
In [112]:
t[:3]
Out[112]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| school_dis | shape_area | shape_leng | geometry_object | geometry_layer | geometry_proj4 | district | diversity_index | |
|---|---|---|---|---|---|---|---|---|
| 0 | 16.0 | 4.676362e+07 | 35848.904605 | POLYGON ((-73.93311862859143 40.69579115384632... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 16 | 95.623066 |
| 1 | 32.0 | 5.189850e+07 | 37251.057847 | POLYGON ((-73.91180710069435 40.70343495202662... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 32 | 204.707566 |
| 2 | 6.0 | 9.634170e+07 | 70447.849084 | POLYGON ((-73.92640556921116 40.87762147653734... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 6 | 254.660597 |
In [113]:
# X = district_map_table.scaled_index
In [114]:
# len(X)
In [115]:
district_map_table.head()
Out[115]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| school_dis | shape_area | shape_leng | geometry_object | geometry_layer | geometry_proj4 | district | diversity_index | WKT | scaled_index | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16.0 | 4.676362e+07 | 35848.904605 | POLYGON ((-73.93311862859143 40.69579115384632... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 16 | 95.623066 | POLYGON ((-73.93311862859143 40.69579115384632... | 33.639628 |
| 1 | 32.0 | 5.189850e+07 | 37251.057847 | POLYGON ((-73.91180710069435 40.70343495202662... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 32 | 204.707566 | POLYGON ((-73.91180710069435 40.70343495202662... | 79.156481 |
| 2 | 6.0 | 9.634170e+07 | 70447.849084 | POLYGON ((-73.92640556921116 40.87762147653734... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 6 | 254.660597 | POLYGON ((-73.92640556921116 40.87762147653734... | 100.000000 |
| 3 | 31.0 | 1.604472e+09 | 434471.412859 | (POLYGON ((-74.05050806403247 40.5664220341608... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 31 | 57.090485 | MULTIPOLYGON (((-74.05050806403247 40.56642203... | 17.561433 |
| 4 | 7.0 | 9.226247e+07 | 65294.452403 | (POLYGON ((-73.89680883223774 40.7958084451597... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 7 | 129.066478 | MULTIPOLYGON (((-73.89680883223774 40.79580844... | 47.594304 |
In [116]:
district_map_table.loc[district_map_table['scaled_index'] < 25, 'FillColor'] = '#ffffcc'
In [117]:
district_map_table.loc[(district_map_table['scaled_index'] >= 25)
& (district_map_table['scaled_index'] < 50), 'FillColor'] = '#fed976'
In [118]:
district_map_table.loc[(district_map_table['scaled_index'] >= 50)
& (district_map_table['scaled_index'] < 75), 'FillColor'] = '#fd8d3c'
In [119]:
district_map_table.loc[(district_map_table['scaled_index'] >= 75), 'FillColor'] = '#e31a1c'
In [120]:
district_map_table
Out[120]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| school_dis | shape_area | shape_leng | geometry_object | geometry_layer | geometry_proj4 | district | diversity_index | WKT | scaled_index | FillColor | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16.0 | 4.676362e+07 | 35848.904605 | POLYGON ((-73.93311862859143 40.69579115384632... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 16 | 95.623066 | POLYGON ((-73.93311862859143 40.69579115384632... | 33.639628 | #fed976 |
| 1 | 32.0 | 5.189850e+07 | 37251.057847 | POLYGON ((-73.91180710069435 40.70343495202662... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 32 | 204.707566 | POLYGON ((-73.91180710069435 40.70343495202662... | 79.156481 | #e31a1c |
| 2 | 6.0 | 9.634170e+07 | 70447.849084 | POLYGON ((-73.92640556921116 40.87762147653734... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 6 | 254.660597 | POLYGON ((-73.92640556921116 40.87762147653734... | 100.000000 | #e31a1c |
| 3 | 31.0 | 1.604472e+09 | 434471.412859 | (POLYGON ((-74.05050806403247 40.5664220341608... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 31 | 57.090485 | MULTIPOLYGON (((-74.05050806403247 40.56642203... | 17.561433 | #ffffcc |
| 4 | 7.0 | 9.226247e+07 | 65294.452403 | (POLYGON ((-73.89680883223774 40.7958084451597... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 7 | 129.066478 | MULTIPOLYGON (((-73.89680883223774 40.79580844... | 47.594304 | #fed976 |
| 5 | 23.0 | 4.740069e+07 | 40317.452033 | (POLYGON ((-73.92044366203014 40.6656262871675... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 23 | 92.098260 | MULTIPOLYGON (((-73.92044366203014 40.66562628... | 32.168859 | #fed976 |
| 6 | 29.0 | 4.201981e+08 | 135035.241651 | POLYGON ((-73.73816144093141 40.72895809117297... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 29 | 61.776259 | POLYGON ((-73.73816144093141 40.72895809117297... | 19.516630 | #ffffcc |
| 7 | 26.0 | 4.247909e+08 | 125677.678898 | POLYGON ((-73.74344992332192 40.77824115291502... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 26 | 31.956221 | POLYGON ((-73.74344992332192 40.77824115291502... | 7.073851 | #ffffcc |
| 8 | 15.0 | 1.961534e+08 | 153439.165680 | POLYGON ((-73.98633135042395 40.69105051012824... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 15 | 80.325170 | POLYGON ((-73.98633135042395 40.69105051012824... | 27.256392 | #fed976 |
| 9 | 17.0 | 1.284414e+08 | 68341.398899 | POLYGON ((-73.92044366203014 40.6656262871675,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 17 | 73.062571 | POLYGON ((-73.92044366203014 40.6656262871675,... | 24.225983 | #ffffcc |
| 10 | 19.0 | 2.034175e+08 | 184183.167312 | (POLYGON ((-73.846736514711 40.60485301485166,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 19 | 73.366731 | MULTIPOLYGON (((-73.846736514711 40.6048530148... | 24.352898 | #ffffcc |
| 11 | 13.0 | 1.048706e+08 | 86635.210559 | POLYGON ((-73.97906084911834 40.70594602894087... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 13 | 51.905636 | POLYGON ((-73.97906084911834 40.70594602894087... | 15.397991 | #ffffcc |
| 12 | 18.0 | 1.751488e+08 | 121184.158477 | (POLYGON ((-73.86706149472118 40.5820879767934... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 18 | 134.915158 | MULTIPOLYGON (((-73.86706149472118 40.58208797... | 50.034738 | #fd8d3c |
| 13 | 20.0 | 2.426965e+08 | 94309.778946 | POLYGON ((-74.02552971543656 40.65147855069281... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 20 | 158.156244 | POLYGON ((-74.02552971543656 40.65147855069281... | 59.732368 | #fd8d3c |
| 14 | 3.0 | 1.134889e+08 | 52072.051321 | POLYGON ((-73.95671863064405 40.78660079332199... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 3 | 45.148505 | POLYGON ((-73.95671863064405 40.78660079332199... | 12.578495 | #ffffcc |
| 15 | 5.0 | 5.251977e+07 | 44469.588221 | POLYGON ((-73.93515659239551 40.83268240623763... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 5 | 74.205055 | POLYGON ((-73.93515659239551 40.83268240623763... | 24.702698 | #ffffcc |
| 16 | 9.0 | 8.341539e+07 | 46648.958586 | POLYGON ((-73.9212971968614 40.85428933985649,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 9 | 171.782841 | POLYGON ((-73.9212971968614 40.85428933985649,... | 65.418234 | #fd8d3c |
| 17 | 1.0 | 3.516075e+07 | 28641.276279 | POLYGON ((-73.97177410965313 40.72582128133706... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 1 | 74.392314 | POLYGON ((-73.97177410965313 40.72582128133706... | 24.780835 | #ffffcc |
| 18 | 14.0 | 1.503102e+08 | 95792.082090 | POLYGON ((-73.95439555417087 40.73911477252251... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 14 | 92.712426 | POLYGON ((-73.95439555417087 40.73911477252251... | 32.425127 | #fed976 |
| 19 | 4.0 | 5.262043e+07 | 52061.828459 | (POLYGON ((-73.92133752419399 40.8008521064970... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 4 | 74.202823 | MULTIPOLYGON (((-73.92133752419399 40.80085210... | 24.701767 | #ffffcc |
| 20 | 10.0 | 2.825415e+08 | 94957.570434 | POLYGON ((-73.86789798628736 40.90294017690526... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 10 | 151.978352 | POLYGON ((-73.86789798628736 40.90294017690526... | 57.154566 | #fd8d3c |
| 21 | 10.0 | 3.282963e+06 | 7883.372664 | (POLYGON ((-73.9089323517538 40.8721573479701,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 10 | 151.978352 | MULTIPOLYGON (((-73.9089323517538 40.872157347... | 57.154566 | #fd8d3c |
| 22 | 12.0 | 6.907182e+07 | 48527.595776 | POLYGON ((-73.88284445574813 40.84781722645163... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 12 | 118.038638 | POLYGON ((-73.88284445574813 40.84781722645163... | 42.992802 | #fed976 |
| 23 | 25.0 | 4.436285e+08 | 175827.007127 | POLYGON ((-73.82049919995312 40.80101146781899... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 25 | 65.688042 | POLYGON ((-73.82049919995312 40.80101146781899... | 21.148870 | #ffffcc |
| 24 | 28.0 | 2.475679e+08 | 114694.912786 | POLYGON ((-73.84485477879177 40.7357514698091,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 28 | 17.399788 | POLYGON ((-73.84485477879177 40.7357514698091,... | 1.000000 | #ffffcc |
| 25 | 24.0 | 3.949782e+08 | 127343.703736 | (POLYGON ((-73.90641585511733 40.7398683641967... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 24 | 189.934931 | MULTIPOLYGON (((-73.90641585511733 40.73986836... | 72.992417 | #fd8d3c |
| 26 | 30.0 | 3.181290e+08 | 150392.978241 | POLYGON ((-73.90647314610101 40.79018117520807... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 30 | 81.290729 | POLYGON ((-73.90647314610101 40.79018117520807... | 27.659283 | #fed976 |
| 27 | 21.0 | 2.101971e+08 | 123858.087345 | POLYGON ((-73.96184657346174 40.62757081710622... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 21 | 59.592857 | POLYGON ((-73.96184657346174 40.62757081710622... | 18.605579 | #ffffcc |
| 28 | 22.0 | 3.855533e+08 | 271718.504936 | (POLYGON ((-73.91990064270161 40.5996005215871... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 22 | 55.823892 | MULTIPOLYGON (((-73.91990064270161 40.59960052... | 17.032931 | #ffffcc |
| 29 | 27.0 | 7.955970e+08 | 589135.490708 | (POLYGON ((-73.82784008953526 40.5887858248046... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 27 | 34.876088 | MULTIPOLYGON (((-73.82784008953526 40.58878582... | 8.292202 | #ffffcc |
| 30 | 11.0 | 3.926651e+08 | 305305.869806 | (POLYGON ((-73.78833349834532 40.8346671297593... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 11 | 54.424475 | MULTIPOLYGON (((-73.78833349834532 40.83466712... | 16.449007 | #ffffcc |
| 31 | 8.0 | 2.588266e+08 | 223080.044096 | (POLYGON ((-73.83979488562292 40.8356192069902... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 8 | 111.027489 | MULTIPOLYGON (((-73.83979488562292 40.83561920... | 40.067313 | #fed976 |
| 32 | 2.0 | 2.804004e+08 | 212406.819436 | (POLYGON ((-74.0438776163991 40.69018767537123... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 2 | 36.504481 | MULTIPOLYGON (((-74.0438776163991 40.690187675... | 8.971669 | #ffffcc |
In [121]:
# EOF error
a_school_map_table = a_school_map_table.rename(columns={
'School Name': 'Name',
})
b_school_map_table = b_school_map_table.rename(columns={
'School Name': 'Name',
})
district_map_table['Name'] = district_map_table['district']
In [122]:
a_school_map_table
Out[122]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
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.dataframe tbody tr th {
vertical-align: top;
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text-align: right;
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</style>
| WKT | Name | total_enrollment | FillColor | |
|---|---|---|---|---|
| 271 | POINT (-73.821532 40.737038) | Townsend Harris High School | 1110 | g |
| 27 | POINT (-73.953276 40.770288) | Eleanor Roosevelt High School | 543 | g |
| 5 | POINT (-73.979581 40.719416) | New Explorations into Science, Technology and ... | 1745 | g |
| 23 | POINT (-73.985723 40.741888) | Baruch College Campus High School | 440 | g |
| 74 | POINT (-73.947171 40.792932) | Young Women's Leadership School | 482 | g |
| 35 | POINT (-74.013921 40.718025) | Stuyvesant High School | 3365 | g |
| 315 | POINT (-74.117086 40.568299) | Staten Island Technical High School | 1312 | g |
| 142 | POINT (-73.889011 40.879958) | Bronx High School of Science | 2979 | g |
| 305 | POINT (-73.926977 40.754975) | Baccalaureate School for Global Education | 518 | g |
| 24 | POINT (-74.002222 40.742512) | N.Y.C. Lab School for Collaborative Studies | 531 | g |
In [123]:
sorted_school_table.tail()
Out[123]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
text-align: right;
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<p>5 rows × 46 columns</p>
| DBN | WKT | School Name | asian_1 | asian_2 | black_1 | black_2 | dbn | economic_need_index | english_language_learners_1 | ... | students_with_disabilities_1 | students_with_disabilities_2 | total_enrollment | white_1 | white_2 | year | diversity_index | scaled_index | district | Total Grads - % of cohort | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 184 | 24Q744 | POINT (-73.871505 40.743228) | VOYAGES Preparatory | 23 | 9.2 | 47 | 18.7 | 24Q744 | 48.7% | 3 | ... | 15 | 6.0 | 251 | 12 | 4.8 | 2016-17 | 210.000000 | 0.035667 | 24 | 9.200000 |
| 220 | 17K568 | POINT (-73.923881 40.666251) | Brownsville Academy High School | 1 | 0.6 | 133 | 85.8 | 17K568 | 67.0% | 3 | ... | 36 | 23.2 | 155 | 0 | 0.0 | 2016-17 | 31.679487 | 0.372449 | 17 | 8.907143 |
| 76 | 05M285 | POINT (-73.939974 40.807692) | Harlem Renaissance High School | 7 | 3.0 | 99 | 42.9 | 05M285 | 77.4% | 31 | ... | 52 | 22.5 | 231 | 3 | 1.3 | 2016-17 | 100.555556 | 0.055118 | 5 | 7.521429 |
| 224 | 18K673 | POINT (-73.920658 40.659914) | East Brooklyn Community High School | 0 | 0.0 | 162 | 83.9 | 18K673 | 67.2% | 12 | ... | 67 | 34.7 | 193 | 4 | 2.1 | 2016-17 | 118.300000 | 0.030082 | 18 | 5.441667 |
| 322 | 32K564 | POINT (-73.915217 40.695875) | Bushwick Community High School | 0 | 0.0 | 67 | 28.4 | 32K564 | 71.0% | 10 | ... | 56 | 23.7 | 236 | 7 | 3.0 | 2016-17 | 234.000000 | 0.226367 | 32 | 5.337500 |
In [124]:
#a_school_map_table['radiusInPixels'] = 10
In [125]:
#b_school_map_table['radiusInPixels'] = 10
In [126]:
district_map_table.iloc[0]
Out[126]:
school_dis 16 shape_area 4.67636e+07 shape_leng 35848.9 geometry_object POLYGON ((-73.93311862859143 40.69579115384632... geometry_layer geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b geometry_proj4 +proj=longlat +ellps=WGS84 +no_defs district 16 diversity_index 95.6231 WKT POLYGON ((-73.93311862859143 40.69579115384632... scaled_index 33.6396 FillColor #fed976 Name 16 Name: 0, dtype: object
In [127]:
# Extract columns
map_table = pd.concat([
district_map_table[['WKT','FillColor','Name']],
a_school_map_table[['WKT','FillColor','Name']],
b_school_map_table[['WKT','FillColor','Name']],
])
In [128]:
#top_schools[:3]
In [129]:
district_map_table
Out[129]:
<style scoped>
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vertical-align: middle;
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vertical-align: top;
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text-align: right;
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</style>
| school_dis | shape_area | shape_leng | geometry_object | geometry_layer | geometry_proj4 | district | diversity_index | WKT | scaled_index | FillColor | Name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16.0 | 4.676362e+07 | 35848.904605 | POLYGON ((-73.93311862859143 40.69579115384632... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 16 | 95.623066 | POLYGON ((-73.93311862859143 40.69579115384632... | 33.639628 | #fed976 | 16 |
| 1 | 32.0 | 5.189850e+07 | 37251.057847 | POLYGON ((-73.91180710069435 40.70343495202662... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 32 | 204.707566 | POLYGON ((-73.91180710069435 40.70343495202662... | 79.156481 | #e31a1c | 32 |
| 2 | 6.0 | 9.634170e+07 | 70447.849084 | POLYGON ((-73.92640556921116 40.87762147653734... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 6 | 254.660597 | POLYGON ((-73.92640556921116 40.87762147653734... | 100.000000 | #e31a1c | 6 |
| 3 | 31.0 | 1.604472e+09 | 434471.412859 | (POLYGON ((-74.05050806403247 40.5664220341608... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 31 | 57.090485 | MULTIPOLYGON (((-74.05050806403247 40.56642203... | 17.561433 | #ffffcc | 31 |
| 4 | 7.0 | 9.226247e+07 | 65294.452403 | (POLYGON ((-73.89680883223774 40.7958084451597... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 7 | 129.066478 | MULTIPOLYGON (((-73.89680883223774 40.79580844... | 47.594304 | #fed976 | 7 |
| 5 | 23.0 | 4.740069e+07 | 40317.452033 | (POLYGON ((-73.92044366203014 40.6656262871675... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 23 | 92.098260 | MULTIPOLYGON (((-73.92044366203014 40.66562628... | 32.168859 | #fed976 | 23 |
| 6 | 29.0 | 4.201981e+08 | 135035.241651 | POLYGON ((-73.73816144093141 40.72895809117297... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 29 | 61.776259 | POLYGON ((-73.73816144093141 40.72895809117297... | 19.516630 | #ffffcc | 29 |
| 7 | 26.0 | 4.247909e+08 | 125677.678898 | POLYGON ((-73.74344992332192 40.77824115291502... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 26 | 31.956221 | POLYGON ((-73.74344992332192 40.77824115291502... | 7.073851 | #ffffcc | 26 |
| 8 | 15.0 | 1.961534e+08 | 153439.165680 | POLYGON ((-73.98633135042395 40.69105051012824... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 15 | 80.325170 | POLYGON ((-73.98633135042395 40.69105051012824... | 27.256392 | #fed976 | 15 |
| 9 | 17.0 | 1.284414e+08 | 68341.398899 | POLYGON ((-73.92044366203014 40.6656262871675,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 17 | 73.062571 | POLYGON ((-73.92044366203014 40.6656262871675,... | 24.225983 | #ffffcc | 17 |
| 10 | 19.0 | 2.034175e+08 | 184183.167312 | (POLYGON ((-73.846736514711 40.60485301485166,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 19 | 73.366731 | MULTIPOLYGON (((-73.846736514711 40.6048530148... | 24.352898 | #ffffcc | 19 |
| 11 | 13.0 | 1.048706e+08 | 86635.210559 | POLYGON ((-73.97906084911834 40.70594602894087... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 13 | 51.905636 | POLYGON ((-73.97906084911834 40.70594602894087... | 15.397991 | #ffffcc | 13 |
| 12 | 18.0 | 1.751488e+08 | 121184.158477 | (POLYGON ((-73.86706149472118 40.5820879767934... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 18 | 134.915158 | MULTIPOLYGON (((-73.86706149472118 40.58208797... | 50.034738 | #fd8d3c | 18 |
| 13 | 20.0 | 2.426965e+08 | 94309.778946 | POLYGON ((-74.02552971543656 40.65147855069281... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 20 | 158.156244 | POLYGON ((-74.02552971543656 40.65147855069281... | 59.732368 | #fd8d3c | 20 |
| 14 | 3.0 | 1.134889e+08 | 52072.051321 | POLYGON ((-73.95671863064405 40.78660079332199... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 3 | 45.148505 | POLYGON ((-73.95671863064405 40.78660079332199... | 12.578495 | #ffffcc | 3 |
| 15 | 5.0 | 5.251977e+07 | 44469.588221 | POLYGON ((-73.93515659239551 40.83268240623763... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 5 | 74.205055 | POLYGON ((-73.93515659239551 40.83268240623763... | 24.702698 | #ffffcc | 5 |
| 16 | 9.0 | 8.341539e+07 | 46648.958586 | POLYGON ((-73.9212971968614 40.85428933985649,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 9 | 171.782841 | POLYGON ((-73.9212971968614 40.85428933985649,... | 65.418234 | #fd8d3c | 9 |
| 17 | 1.0 | 3.516075e+07 | 28641.276279 | POLYGON ((-73.97177410965313 40.72582128133706... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 1 | 74.392314 | POLYGON ((-73.97177410965313 40.72582128133706... | 24.780835 | #ffffcc | 1 |
| 18 | 14.0 | 1.503102e+08 | 95792.082090 | POLYGON ((-73.95439555417087 40.73911477252251... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 14 | 92.712426 | POLYGON ((-73.95439555417087 40.73911477252251... | 32.425127 | #fed976 | 14 |
| 19 | 4.0 | 5.262043e+07 | 52061.828459 | (POLYGON ((-73.92133752419399 40.8008521064970... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 4 | 74.202823 | MULTIPOLYGON (((-73.92133752419399 40.80085210... | 24.701767 | #ffffcc | 4 |
| 20 | 10.0 | 2.825415e+08 | 94957.570434 | POLYGON ((-73.86789798628736 40.90294017690526... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 10 | 151.978352 | POLYGON ((-73.86789798628736 40.90294017690526... | 57.154566 | #fd8d3c | 10 |
| 21 | 10.0 | 3.282963e+06 | 7883.372664 | (POLYGON ((-73.9089323517538 40.8721573479701,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 10 | 151.978352 | MULTIPOLYGON (((-73.9089323517538 40.872157347... | 57.154566 | #fd8d3c | 10 |
| 22 | 12.0 | 6.907182e+07 | 48527.595776 | POLYGON ((-73.88284445574813 40.84781722645163... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 12 | 118.038638 | POLYGON ((-73.88284445574813 40.84781722645163... | 42.992802 | #fed976 | 12 |
| 23 | 25.0 | 4.436285e+08 | 175827.007127 | POLYGON ((-73.82049919995312 40.80101146781899... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 25 | 65.688042 | POLYGON ((-73.82049919995312 40.80101146781899... | 21.148870 | #ffffcc | 25 |
| 24 | 28.0 | 2.475679e+08 | 114694.912786 | POLYGON ((-73.84485477879177 40.7357514698091,... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 28 | 17.399788 | POLYGON ((-73.84485477879177 40.7357514698091,... | 1.000000 | #ffffcc | 28 |
| 25 | 24.0 | 3.949782e+08 | 127343.703736 | (POLYGON ((-73.90641585511733 40.7398683641967... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 24 | 189.934931 | MULTIPOLYGON (((-73.90641585511733 40.73986836... | 72.992417 | #fd8d3c | 24 |
| 26 | 30.0 | 3.181290e+08 | 150392.978241 | POLYGON ((-73.90647314610101 40.79018117520807... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 30 | 81.290729 | POLYGON ((-73.90647314610101 40.79018117520807... | 27.659283 | #fed976 | 30 |
| 27 | 21.0 | 2.101971e+08 | 123858.087345 | POLYGON ((-73.96184657346174 40.62757081710622... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 21 | 59.592857 | POLYGON ((-73.96184657346174 40.62757081710622... | 18.605579 | #ffffcc | 21 |
| 28 | 22.0 | 3.855533e+08 | 271718.504936 | (POLYGON ((-73.91990064270161 40.5996005215871... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 22 | 55.823892 | MULTIPOLYGON (((-73.91990064270161 40.59960052... | 17.032931 | #ffffcc | 22 |
| 29 | 27.0 | 7.955970e+08 | 589135.490708 | (POLYGON ((-73.82784008953526 40.5887858248046... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 27 | 34.876088 | MULTIPOLYGON (((-73.82784008953526 40.58878582... | 8.292202 | #ffffcc | 27 |
| 30 | 11.0 | 3.926651e+08 | 305305.869806 | (POLYGON ((-73.78833349834532 40.8346671297593... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 11 | 54.424475 | MULTIPOLYGON (((-73.78833349834532 40.83466712... | 16.449007 | #ffffcc | 11 |
| 31 | 8.0 | 2.588266e+08 | 223080.044096 | (POLYGON ((-73.83979488562292 40.8356192069902... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 8 | 111.027489 | MULTIPOLYGON (((-73.83979488562292 40.83561920... | 40.067313 | #fed976 | 8 |
| 32 | 2.0 | 2.804004e+08 | 212406.819436 | (POLYGON ((-74.0438776163991 40.69018767537123... | geo_export_42eedaf7-5cda-4346-b866-55c3ecc08a3b | +proj=longlat +ellps=WGS84 +no_defs | 2 | 36.504481 | MULTIPOLYGON (((-74.0438776163991 40.690187675... | 8.971669 | #ffffcc | 2 |
In [130]:
type(district_map_table)
Out[130]:
geotable.GeoTable
In [131]:
target_path = target_folder + '/choropleth.csv'
map_table.to_csv(target_path, index=False)
print('a_geotable_path = %s' % target_path)
a_geotable_path = /tmp/choropleth.csv
In [132]:
map_table
Out[132]:
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| WKT | FillColor | Name | |
|---|---|---|---|
| 0 | POLYGON ((-73.93311862859143 40.69579115384632... | #fed976 | 16 |
| 1 | POLYGON ((-73.91180710069435 40.70343495202662... | #e31a1c | 32 |
| 2 | POLYGON ((-73.92640556921116 40.87762147653734... | #e31a1c | 6 |
| 3 | MULTIPOLYGON (((-74.05050806403247 40.56642203... | #ffffcc | 31 |
| 4 | MULTIPOLYGON (((-73.89680883223774 40.79580844... | #fed976 | 7 |
| 5 | MULTIPOLYGON (((-73.92044366203014 40.66562628... | #fed976 | 23 |
| 6 | POLYGON ((-73.73816144093141 40.72895809117297... | #ffffcc | 29 |
| 7 | POLYGON ((-73.74344992332192 40.77824115291502... | #ffffcc | 26 |
| 8 | POLYGON ((-73.98633135042395 40.69105051012824... | #fed976 | 15 |
| 9 | POLYGON ((-73.92044366203014 40.6656262871675,... | #ffffcc | 17 |
| 10 | MULTIPOLYGON (((-73.846736514711 40.6048530148... | #ffffcc | 19 |
| 11 | POLYGON ((-73.97906084911834 40.70594602894087... | #ffffcc | 13 |
| 12 | MULTIPOLYGON (((-73.86706149472118 40.58208797... | #fd8d3c | 18 |
| 13 | POLYGON ((-74.02552971543656 40.65147855069281... | #fd8d3c | 20 |
| 14 | POLYGON ((-73.95671863064405 40.78660079332199... | #ffffcc | 3 |
| 15 | POLYGON ((-73.93515659239551 40.83268240623763... | #ffffcc | 5 |
| 16 | POLYGON ((-73.9212971968614 40.85428933985649,... | #fd8d3c | 9 |
| 17 | POLYGON ((-73.97177410965313 40.72582128133706... | #ffffcc | 1 |
| 18 | POLYGON ((-73.95439555417087 40.73911477252251... | #fed976 | 14 |
| 19 | MULTIPOLYGON (((-73.92133752419399 40.80085210... | #ffffcc | 4 |
| 20 | POLYGON ((-73.86789798628736 40.90294017690526... | #fd8d3c | 10 |
| 21 | MULTIPOLYGON (((-73.9089323517538 40.872157347... | #fd8d3c | 10 |
| 22 | POLYGON ((-73.88284445574813 40.84781722645163... | #fed976 | 12 |
| 23 | POLYGON ((-73.82049919995312 40.80101146781899... | #ffffcc | 25 |
| 24 | POLYGON ((-73.84485477879177 40.7357514698091,... | #ffffcc | 28 |
| 25 | MULTIPOLYGON (((-73.90641585511733 40.73986836... | #fd8d3c | 24 |
| 26 | POLYGON ((-73.90647314610101 40.79018117520807... | #fed976 | 30 |
| 27 | POLYGON ((-73.96184657346174 40.62757081710622... | #ffffcc | 21 |
| 28 | MULTIPOLYGON (((-73.91990064270161 40.59960052... | #ffffcc | 22 |
| 29 | MULTIPOLYGON (((-73.82784008953526 40.58878582... | #ffffcc | 27 |
| 30 | MULTIPOLYGON (((-73.78833349834532 40.83466712... | #ffffcc | 11 |
| 31 | MULTIPOLYGON (((-73.83979488562292 40.83561920... | #fed976 | 8 |
| 32 | MULTIPOLYGON (((-74.0438776163991 40.690187675... | #ffffcc | 2 |
| 271 | POINT (-73.821532 40.737038) | g | Townsend Harris High School |
| 27 | POINT (-73.953276 40.770288) | g | Eleanor Roosevelt High School |
| 5 | POINT (-73.979581 40.719416) | g | New Explorations into Science, Technology and ... |
| 23 | POINT (-73.985723 40.741888) | g | Baruch College Campus High School |
| 74 | POINT (-73.947171 40.792932) | g | Young Women's Leadership School |
| 35 | POINT (-74.013921 40.718025) | g | Stuyvesant High School |
| 315 | POINT (-74.117086 40.568299) | g | Staten Island Technical High School |
| 142 | POINT (-73.889011 40.879958) | g | Bronx High School of Science |
| 305 | POINT (-73.926977 40.754975) | g | Baccalaureate School for Global Education |
| 24 | POINT (-74.002222 40.742512) | g | N.Y.C. Lab School for Collaborative Studies |
| 203 | POINT (-73.992151 40.642619) | r | West Brooklyn Community High School |
| 89 | POINT (-73.905389 40.818703) | r | Jill Chaifetz Transfer High School |
| 104 | POINT (-73.85593 40.821218) | r | Bronx Community High School |
| 252 | POINT (-73.904143 40.677528) | r | Aspirations Diploma Plus High School |
| 90 | POINT (-73.919949 40.818645) | r | Bronx Haven High School |
| 184 | POINT (-73.871505 40.743228) | r | VOYAGES Preparatory |
| 220 | POINT (-73.923881 40.666251) | r | Brownsville Academy High School |
| 76 | POINT (-73.939974 40.807692) | r | Harlem Renaissance High School |
| 224 | POINT (-73.920658 40.659914) | r | East Brooklyn Community High School |
| 322 | POINT (-73.915217 40.695875) | r | Bushwick Community High School |
In [133]:
#%matplotlib inline
#t['economic_need_index'].plot(kind='bar')
In [134]:
sorted_school_table.to_csv('data.csv', index=False)
In [135]:
sorted_school_table.iloc[0]
Out[135]:
DBN 25Q525 WKT POINT (-73.821532 40.737038) School Name Townsend Harris High School asian_1 644 asian_2 58 black_1 60 black_2 5.4 dbn 25Q525 economic_need_index 26.1% english_language_learners_1 0 english_language_learners_2 0 female_1 770 female_2 69.4 grade_1 0 grade_10 295 grade_11 290 grade_12 261 grade_2 0 grade_3 0 grade_4 0 grade_5 0 grade_6 0 grade_7 0 grade_8 0 grade_9 264 grade_k 0 grade_pk_half_day_full_day 0 hispanic_1 127 hispanic_2 11.4 male_1 340 male_2 30.6 multiple_race_categories_not_represented_1 45 multiple_race_categories_not_represented_2 4.1 poverty_1 599 poverty_2 54 school_name Townsend Harris High School students_with_disabilities_1 29 students_with_disabilities_2 2.6 total_enrollment 1110 white_1 234 white_2 21.1 year 2016-17 diversity_index 16.5976 scaled_index 0.016038 district 25 Total Grads - % of cohort 99.5526 Name: 271, dtype: object
In [163]:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Fixing random state for reproducibility
np.random.seed(19680801)
x = sorted_school_table['scaled_index'].values
y = sorted_school_table[selected_outcome].values
#colors = np.random.rand(N)
#area = (30 * np.random.rand(N))**2 # 0 to 15 point radii
x.sort()
y.sort()
x = x[:-5]
y = y[:-5]
np.random.shuffle(x)
np.random.shuffle(y)
#axes = plt.scatter(x, y, s=area, c=colors, alpha=0.5)
axes = plt.scatter(x, y, alpha=0.5)
plt.ylabel(selected_outcome)
plt.xlabel('Diversity Index')
plt.show()
In [158]:
x[:-5]
Out[158]:
array([0. , 0.00048444, 0.00067926, 0.00171858, 0.00172513,
0.00233685, 0.00283372, 0.00295918, 0.00322157, 0.00386555,
0.00386874, 0.00498669, 0.00622097, 0.0062449 , 0.00684392,
0.00704525, 0.00725714, 0.0072967 , 0.00742497, 0.00743252,
0.00752834, 0.00757928, 0.00782313, 0.00805081, 0.01009667,
0.01036735, 0.01075276, 0.01085343, 0.01086899, 0.01129009,
0.01134694, 0.01308784, 0.01321995, 0.01382189, 0.01382189,
0.01383673, 0.01416625, 0.01433895, 0.01482993, 0.01534131,
0.01537783, 0.01555985, 0.01572012, 0.01591837, 0.016038 ,
0.01659864, 0.01680816, 0.01711565, 0.01734694, 0.0174664 ,
0.01759184, 0.0177551 , 0.01782428, 0.01788018, 0.01891582,
0.01895201, 0.0202551 , 0.02071429, 0.02102041, 0.02128728,
0.02141399, 0.02183673, 0.0219666 , 0.02204898, 0.02232143,
0.02241983, 0.02281179, 0.02323129, 0.02363265, 0.02384615,
0.02399417, 0.02431973, 0.02437164, 0.02450387, 0.02469388,
0.02472109, 0.02484694, 0.02504937, 0.02549165, 0.02555102,
0.02586271, 0.02613703, 0.02626586, 0.02638655, 0.02648163,
0.02744898, 0.02747681, 0.02813776, 0.02911079, 0.02913931,
0.02994169, 0.02996708, 0.03008163, 0.03068027, 0.03179592,
0.032 , 0.03204082, 0.03227041, 0.03262857, 0.03291545,
0.03397959, 0.03412245, 0.03417312, 0.03438424, 0.0352076 ,
0.03566719, 0.03581096, 0.03593407, 0.03664399, 0.03683673,
0.03693878, 0.03729592, 0.0373399 , 0.03745306, 0.03823673,
0.03830904, 0.03875283, 0.03946429, 0.03976676, 0.0404898 ,
0.04061224, 0.04076531, 0.04132245, 0.04171429, 0.04197279,
0.04197668, 0.04198516, 0.04209184, 0.04239332, 0.04310832,
0.04367347, 0.04404082, 0.04416327, 0.04428571, 0.04443878,
0.04533528, 0.0457398 , 0.0458657 , 0.04629738, 0.04658163,
0.04658892, 0.04664723, 0.04710204, 0.04734694, 0.04745826,
0.04750806, 0.04771961, 0.04790816, 0.04820408, 0.04909621,
0.04930612, 0.04979592, 0.05049563, 0.05058957, 0.05102041,
0.05129252, 0.05198711, 0.0522449 , 0.0524898 , 0.05322981,
0.05368805, 0.05413265, 0.05456498, 0.05469388, 0.05511774,
0.0552381 , 0.05536735, 0.05546939, 0.05594499, 0.0564723 ,
0.05653061, 0.05653061, 0.05694952, 0.05725417, 0.0577551 ,
0.05877551, 0.05908764, 0.05931973, 0.05995198, 0.06020408,
0.06068323, 0.06081633, 0.06096939, 0.06102041, 0.06122449,
0.06138776, 0.06176871, 0.06304922, 0.06317784, 0.06387755,
0.06412965, 0.0644898 , 0.06503759, 0.06513806, 0.06568206,
0.06618524, 0.06661224, 0.06708283, 0.06718367, 0.06773469,
0.06843537, 0.06954082, 0.07 , 0.07007653, 0.07013605,
0.07036014, 0.07038265, 0.07061224, 0.07232009, 0.07315789,
0.07338776, 0.07346939, 0.07393586, 0.07469388, 0.07510204,
0.07518367, 0.0755102 , 0.07778426, 0.0784898 , 0.07899529,
0.08014577, 0.08094388, 0.08102041, 0.08142857, 0.08399417,
0.085 , 0.08521193, 0.08605965, 0.08622449, 0.08661808,
0.09 , 0.09125364, 0.09234694, 0.09295918, 0.09387755,
0.09428571, 0.09473469, 0.09476809, 0.09533752, 0.09622449,
0.09646782, 0.09721707, 0.09938776, 0.10178108, 0.10261596,
0.10271137, 0.10390895, 0.10459184, 0.10538776, 0.10955102,
0.10979592, 0.11010204, 0.11077551, 0.11236735, 0.11346939,
0.11444898, 0.11680272, 0.11714286, 0.11802721, 0.11886827,
0.11959184, 0.12027211, 0.12054422, 0.12106122, 0.12240816,
0.12436735, 0.1244898 , 0.12639456, 0.12767347, 0.13097959,
0.13146939, 0.13428571, 0.13795918, 0.142 , 0.14346939,
0.14438776, 0.14515306, 0.14612245, 0.1484898 , 0.15632653,
0.15658892, 0.15755102, 0.15825073, 0.15947522, 0.15965015,
0.16153061, 0.16442177, 0.16638484, 0.16708455, 0.16778426,
0.17204082, 0.17420408, 0.17740525, 0.17934694, 0.17959184,
0.18081633, 0.18265306, 0.18461224, 0.19102041, 0.19321429,
0.19530612, 0.21387755, 0.21681633, 0.2255102 , 0.22636735,
0.24877551, 0.25428571, 0.27295918, 0.28367347, 0.29806122,
0.29979592, 0.33632653, 0.36204082, 0.37244898, 0.37346939,
0.38244898, 0.38979592])
In [137]:
# plt.scatter?
In [138]:
# Save file to target folder to include it in the result download
target_path = target_folder + '/c.png'
figure = axes.get_figure()
figure.savefig(target_path)
print(f'c_image_path = {target_path}')
c_image_path = /tmp/c.png
In [139]:
from sklearn.linear_model import LinearRegression
model = LinearRegression()
In [146]:
X = sorted_school_table[['scaled_index']].values
X
Out[146]:
array([[1.60380014e-02],
[1.07527601e-02],
[2.47210884e-02],
[4.98669033e-03],
[3.82367347e-02],
[2.38461538e-02],
[5.65306122e-02],
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[3.69387755e-02],
[9.64678179e-02],
[2.91393079e-02],
[1.12367347e-01],
[3.26285714e-02],
[5.87755102e-02],
[2.13877551e-01],
[8.66180758e-02],
[7.51020408e-02],
[7.52834467e-03],
[1.05387755e-01],
[5.32298137e-02],
[5.45649839e-02],
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[4.90962099e-02],
[1.43389530e-02],
[1.02711370e-01],
[5.12925170e-02],
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[6.10204082e-02],
[1.48489796e-01],
[2.23214286e-02],
[6.30492197e-02],
[1.30979592e-01],
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[1.71156463e-02],
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[8.14285714e-02],
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[2.26367347e-01]])
In [147]:
y = sorted_school_table[selected_outcome].values
y
Out[147]:
array([99.55263158, 99.3452381 , 98.50344828, 98.33404255, 98.08157895,
97.70862069, 97.53928571, 97.5031746 , 96.92105263, 96.64081633,
96.31333333, 96.275 , 95.79166667, 95.77837838, 95.24761905,
95.1106383 , 94.78888889, 94.758 , 94.6962963 , 94.59183673,
93.81666667, 93.55454545, 93.32105263, 92.73174603, 92.672 ,
92.33513514, 91.9875 , 91.92545455, 91.86382979, 91.7 ,
91.41904762, 91.33913043, 91.12857143, 90.55087719, 90.4625 ,
89.68333333, 89.025 , 88.99302326, 88.825 , 88.68372093,
88.54651163, 87.87647059, 86.53095238, 86.52380952, 86.11666667,
85.51111111, 85.46851852, 85.35833333, 85.14516129, 85.0862069 ,
84.9968254 , 84.62173913, 84.244 , 84.20952381, 83.67906977,
83.61666667, 83.59583333, 83.46666667, 83.4 , 83.33928571,
83.07608696, 82.95714286, 82.19756098, 81.58684211, 81.55882353,
81.48181818, 81.45925926, 81.45686275, 80.99090909, 80.90571429,
80.79183673, 80.60285714, 80.45172414, 80.388 , 80.15714286,
79.91052632, 79.75714286, 79.50784314, 79.148 , 79.07818182,
78.83617021, 78.81166667, 78.76666667, 78.65 , 78.40263158,
78.19166667, 78.03571429, 77.84657534, 77.22916667, 77.04444444,
76.96666667, 76.75 , 76.71777778, 76.66 , 76.56410256,
75.81746032, 75.55714286, 75.15945946, 75.01764706, 74.84736842,
74.73333333, 74.65238095, 74.3875 , 74.25357143, 74.1 ,
73.9 , 73.64222222, 73.42051282, 73.4012987 , 73.21842105,
73.1952381 , 73.17692308, 73.04626866, 72.91621622, 72.9027027 ,
72.80833333, 72.47222222, 72.17916667, 72.14857143, 72.08928571,
71.74166667, 71.65531915, 71.43571429, 71.40526316, 71.31666667,
71.19 , 70.96428571, 70.91267606, 70.82162162, 70.81714286,
70.80769231, 70.7375 , 70.7325 , 70.5212766 , 70.4625 ,
70.41071429, 70.34230769, 70.19047619, 70.17313433, 70.0875 ,
70.02077922, 69.97058824, 69.77857143, 69.45675676, 68.76451613,
68.65102041, 68.64285714, 68.46363636, 68.36923077, 68.15 ,
68.09714286, 67.5952381 , 67.44 , 67.36530612, 67.29152542,
67.20952381, 67.01384615, 66.93214286, 66.63913043, 66.3875 ,
66.124 , 65.57857143, 65.41086957, 65.34285714, 64.93877551,
64.83333333, 64.75526316, 64.74705882, 64.74166667, 64.43225806,
64.375 , 64.2 , 64.18703704, 64.09459459, 63.91111111,
63.88461538, 63.88039216, 63.85882353, 63.81020408, 63.72 ,
63.53246753, 63.46875 , 63.32608696, 63.31071429, 63.16140351,
62.98888889, 62.86875 , 62.74761905, 62.53584906, 62.49268293,
62.016 , 61.99555556, 61.89642857, 61.86190476, 61.75555556,
61.6952381 , 61.664 , 61.56551724, 61.27066667, 61.16461538,
61.15813953, 60.92222222, 60.83658537, 60.49047619, 60.36458333,
60.10714286, 60.09791667, 59.87922078, 59.75119048, 59.66964286,
59.32244898, 59.12 , 59.08333333, 59.08 , 59.07843137,
58.78571429, 58.70784314, 58.7 , 58.63773585, 58.62653061,
58.46666667, 58.42909091, 58.39545455, 58.35306122, 58.14285714,
58.14166667, 58.13409091, 57.95 , 57.88431373, 57.85185185,
57.59090909, 57.14428571, 57.01463415, 56.75263158, 56.5 ,
56.44230769, 56.2627907 , 56.26219512, 55.89807692, 55.60140845,
55.45652174, 55.25974026, 55.06428571, 55.01369863, 54.96 ,
54.94677419, 54.84166667, 54.67272727, 54.35555556, 54.316 ,
53.25625 , 53.16 , 53.08285714, 52.47460317, 52.31086957,
51.9047619 , 51.02037037, 50.82077922, 50.75584416, 50.21818182,
50.12857143, 49.90238095, 49.81547619, 49.15106383, 49.07142857,
48.68181818, 48.52413793, 48.42142857, 47.92142857, 47.77162162,
47.73921569, 47.30701754, 47.28965517, 46.77777778, 46.77446809,
46.6 , 46.35584416, 46.2012987 , 46.17012987, 45.02857143,
44.25324675, 42.90363636, 42.49111111, 42.44675325, 41.92678571,
40.85789474, 39.55507246, 38.94615385, 37.96721311, 37.47346939,
37.20350877, 36.28032787, 36.25862069, 33.62 , 27.39459459,
27.3 , 26.96666667, 25.82653061, 24.84166667, 20.28333333,
20.256 , 19.83888889, 19.64915254, 17.06666667, 15.86046512,
15.65 , 15.41034483, 15.38 , 14.46428571, 14.37826087,
13.53888889, 13.47857143, 12.95483871, 12.92916667, 10.68333333,
10.335 , 9.5 , 9.2 , 8.90714286, 7.52142857,
5.44166667, 5.3375 ])
In [148]:
model.fit(X, y)
Out[148]:
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
normalize=False)
In [ ]:
from sklearn.model_selection import cross_val_score
models = []
scores = []
def train(model, X):
model.fit(X, y)
models.append(model)
score = cross_val_score(
model, X, y, cv=3,
scoring='neg_mean_absolute_error',
).mean()
scores.append(score)
return score
In [ ]:
model_scores = cross_val_score(model, X, y, cv=3, scoring='neg_mean_absolute_error')
print('average_negative_mean_error = %s' % np.mean(model_scores))
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