-
Notifications
You must be signed in to change notification settings - Fork 8
Expand file tree
/
Copy pathnyc_zipcode_list.py
More file actions
213 lines (194 loc) · 8.11 KB
/
nyc_zipcode_list.py
File metadata and controls
213 lines (194 loc) · 8.11 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
import pandas as pd
from bs4 import BeautifulSoup
# from http://stackoverflow.com/questions/259091/how-can-i-scrape-an-html-table-to-csv/259100
def table2csv(html_txt):
csvs = []
soup = BeautifulSoup(html_txt, "lxml")
tables = soup.findAll('table')
for table in tables:
csv = ''
rows = table.findAll('tr')
row_spans = []
do_ident = False
for tr in rows:
cols = tr.findAll(['th','td'])
for cell in cols:
colspan = int(cell.get('colspan',1))
rowspan = int(cell.get('rowspan',1))
if do_ident:
do_ident = False
csv += ','*(len(row_spans))
if rowspan > 1: row_spans.append(rowspan)
csv += '"{text}"'.format(text=cell.text) + ','*(colspan)
if row_spans:
for i in range(len(row_spans)-1,-1,-1):
row_spans[i] -= 1
if row_spans[i] < 1: row_spans.pop()
do_ident = True if row_spans else False
csv += '\n'
csvs.append(csv)
#print csv
return '\n\n'.join(csvs)
def create_zipcode_file():
# from https://www.health.ny.gov/statistics/cancer/registry/appendix/neighborhoods.htm
body = """\
<table summary=" ">
<tbody><tr>
<th id="header1" abbr="Borough">Borough</th>
<th id="header2" abbr="Neighborhood">Neighborhood</th>
<th id="header3" abbr="ZIP Codes">ZIP Codes</th>
</tr><tr>
<td headers="header1" rowspan="7">Bronx</td>
<td headers="header2"> Central Bronx</td>
<td headers="header3"> 10453, 10457, 10460</td>
</tr><tr>
<td headers="header2"> Bronx Park and Fordham</td>
<td headers="header3"> 10458, 10467, 10468</td>
</tr><tr>
<td headers="header2"> High Bridge and Morrisania</td>
<td headers="header3"> 10451, 10452, 10456</td>
</tr><tr>
<td headers="header2"> Hunts Point and Mott Haven</td>
<td headers="header3"> 10454, 10455, 10459, 10474</td>
</tr><tr>
<td headers="header2"> Kingsbridge and Riverdale</td>
<td headers="header3"> 10463, 10471</td>
</tr><tr>
<td headers="header2"> Northeast Bronx</td>
<td headers="header3"> 10466, 10469, 10470, 10475</td>
</tr><tr>
<td headers="header2"> Southeast Bronx</td>
<td headers="header3"> 10461, 10462,10464, 10465, 10472, 10473</td>
</tr><tr>
<td headers="header1" rowspan="11">Brooklyn</td>
<td headers="header2"> Central Brooklyn</td>
<td headers="header3"> 11212, 11213, 11216, 11233, 11238</td>
</tr><tr>
<td headers="header2"> Southwest Brooklyn</td>
<td headers="header3"> 11209, 11214, 11228</td>
</tr><tr>
<td headers="header2"> Borough Park</td>
<td headers="header3"> 11204, 11218, 11219, 11230</td>
</tr><tr>
<td headers="header2"> Canarsie and Flatlands</td>
<td headers="header3"> 11234, 11236, 11239</td>
</tr><tr>
<td headers="header2"> Southern Brooklyn</td>
<td headers="header3"> 11223, 11224, 11229, 11235</td>
</tr><tr>
<td headers="header2"> Northwest Brooklyn</td>
<td headers="header3"> 11201, 11205, 11215, 11217, 11231</td>
</tr><tr>
<td headers="header2"> Flatbush</td>
<td headers="header3"> 11203, 11210, 11225, 11226</td>
</tr><tr>
<td headers="header2"> East New York and New Lots</td>
<td headers="header3"> 11207, 11208</td>
</tr><tr>
<td headers="header2"> Greenpoint</td>
<td headers="header3"> 11211, 11222</td>
</tr><tr>
<td headers="header2"> Sunset Park</td>
<td headers="header3"> 11220, 11232</td>
</tr><tr>
<td headers="header2"> Bushwick and Williamsburg</td>
<td headers="header3"> 11206, 11221, 11237</td>
</tr><tr>
<td headers="header1" rowspan="10">Manhattan</td>
<td headers="header2"> Central Harlem</td>
<td headers="header3"> 10026, 10027, 10030, 10037, 10039</td>
</tr><tr>
<td headers="header2"> Chelsea and Clinton</td>
<td headers="header3"> 10001, 10011, 10018, 10019, 10020, 10036</td>
</tr><tr>
<td headers="header2"> East Harlem</td>
<td headers="header3"> 10029, 10035</td>
</tr><tr>
<td headers="header2"> Gramercy Park and Murray Hill</td>
<td headers="header3"> 10010, 10016, 10017, 10022</td>
</tr><tr>
<td headers="header2"> Greenwich Village and Soho</td>
<td headers="header3"> 10012, 10013, 10014</td>
</tr><tr>
<td headers="header2"> Lower Manhattan</td>
<td headers="header3"> 10004, 10005, 10006, 10007, 10038, 10280</td>
</tr><tr>
<td headers="header2"> Lower East Side</td>
<td headers="header3"> 10002, 10003, 10009</td>
</tr><tr>
<td headers="header2"> Upper East Side</td>
<td headers="header3"> 10021, 10028, 10044, 10065, 10075, 10128</td>
</tr><tr>
<td headers="header2"> Upper West Side</td>
<td headers="header3"> 10023, 10024, 10025</td>
</tr><tr>
<td headers="header2"> Inwood and Washington Heights</td>
<td headers="header3"> 10031, 10032, 10033, 10034, 10040</td>
</tr><tr>
<td headers="header1" rowspan="10">Queens</td>
<td headers="header2"> Northeast Queens</td>
<td headers="header3"> 11361, 11362, 11363, 11364</td>
</tr><tr>
<td headers="header2"> North Queens</td>
<td headers="header3"> 11354, 11355, 11356, 11357, 11358, 11359, 11360</td>
</tr><tr>
<td headers="header2"> Central Queens</td>
<td headers="header3"> 11365, 11366, 11367</td>
</tr><tr>
<td headers="header2"> Jamaica</td>
<td headers="header3"> 11412, 11423, 11432, 11433, 11434, 11435, 11436</td>
</tr><tr>
<td headers="header2"> Northwest Queens</td>
<td headers="header3"> 11101, 11102, 11103, 11104, 11105, 11106</td>
</tr><tr>
<td headers="header2"> West Central Queens</td>
<td headers="header3"> 11374, 11375, 11379, 11385</td>
</tr><tr>
<td headers="header2"> Rockaways</td>
<td headers="header3"> 11691, 11692, 11693, 11694, 11695, 11697</td>
</tr><tr>
<td headers="header2"> Southeast Queens</td>
<td headers="header3"> 11004, 11005, 11411, 11413, 11422, 11426, 11427, 11428, 11429</td>
</tr><tr>
<td headers="header2"> Southwest Queens</td>
<td headers="header3"> 11414, 11415, 11416, 11417, 11418, 11419, 11420, 11421</td>
</tr><tr>
<td headers="header2"> West Queens</td>
<td headers="header3"> 11368, 11369, 11370, 11372, 11373, 11377, 11378</td>
</tr><tr>
<td headers="header1" rowspan="4">Staten Island</td>
<td headers="header2"> Port Richmond</td>
<td headers="header3"> 10302, 10303, 10310</td>
</tr><tr>
<td headers="header2"> South Shore</td>
<td headers="header3"> 10306, 10307, 10308, 10309, 10312</td>
</tr><tr>
<td headers="header2"> Stapleton and St. George</td>
<td headers="header3"> 10301, 10304, 10305</td>
</tr><tr>
<td headers="header2"> Mid-Island</td>
<td headers="header3"> 10314</td>
</tr>
</tbody></table>
"""
from io import StringIO
df = pd.read_csv(StringIO(table2csv(body)))
df.fillna(method="ffill", inplace=True)
df.drop("Unnamed: 3", axis=1, inplace=True)
df.rename(columns={"ZIP Codes": "ZIP"}, inplace=True)
df.set_index(["Borough", "Neighborhood"], inplace=True)
zips = df.ZIP.str.split(", ?", expand=True)
zips.reset_index(inplace=True)
zips1 = pd.melt(zips, ["Borough", "Neighborhood"])
zips1.drop("variable", axis=1, inplace=True)
zips1.dropna(inplace=True)
zips1["Neighborhood"] = zips1["Neighborhood"].str.strip()
zips1.set_index(["Borough", "Neighborhood"], inplace=True)
final = zips1.astype(int)
final.sort_index(inplace=True)
final.to_csv("nyc_zipcodes.csv")
if __name__ == '__main__':
import os
if not os.path.isfile("nyc_zipcodes.csv"):
create_zipcode_file()
df = pd.read_csv("nyc_zipcodes.csv", index_col=[0, 1])