In [9]:
import requests
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import csv
import seaborn as sns
font = {'family' : 'Arial',
'weight' : 'bold',
'size' : 25}
matplotlib.rc('font', **font)
US census provides shapefiles for every zip code in the nation. Typically, many people use this website. However, US census file is very big, and the website cannot handle it. This page provides solutions:
gdal$ ogr2ogr -f GeoJSON -t_srs crs:84 [name].geojson [name].shp where [name] is the name of the file. Make sure that you have all the unzipped shapefiles with [name].shp in the same folder.
In [10]:
# Check if this can be rendered
import folium
m = folium.Map(location=[37.769959, -122.448679], zoom_start=9)
geo_path = 'zip.geojson'
m.geo_json(geo_path= geo_path)
m.create_map('zip.html')
In [11]:
# list of major 50 cities
df_ct = pd.read_csv('citi_list.csv')
df_ct.head()
Out[11]:
city
state
zipcodes
0
Baltimore
MD
21201, 21202, 21205, 21206, 21207, 21208, 2120...
1
Boston
MA
02108, 02109, 02110, 02111, 02113, 02114, 0211...
2
Chicago
IL
60018, 60068, 60176, 60601, 60602, 60603, 6060...
3
Detroit
MI
48201, 48202, 48203, 48204, 48205, 48206, 4820...
4
Los Angeles
CA
90003, 90004, 90005, 90006, 90008, 90007, 9001...
In [12]:
# There are many zip codes in a city, so we split the zipcodes and replicate the rows
df_city = pd.concat([pd.DataFrame([df_ct['city'].values[i]]*len(df_ct['zipcodes'][i].split(', '))) for i in range(len(df_ct))],ignore_index=True)
df_state = pd.concat([pd.DataFrame([df_ct['state'].values[i]]*len(df_ct['zipcodes'][i].split(', '))) for i in range(len(df_ct))],ignore_index=True)
df_zipcodes = pd.concat([pd.DataFrame(df_ct['zipcodes'][i].split(', ')) for i in range(len(df_ct))],ignore_index=True)
In [13]:
# put them together and name the columns
df_city_zip = pd.concat([df_city,df_state,df_zipcodes],axis=1)
df_city_zip.columns = df_ct.columns
In [14]:
df_city_zip.head()
Out[14]:
city
state
zipcodes
0
Baltimore
MD
21201
1
Baltimore
MD
21202
2
Baltimore
MD
21205
3
Baltimore
MD
21206
4
Baltimore
MD
21207
In [17]:
df_city_zip.to_csv('city_zip_df.csv',encoding='utf-8',index=False)
In [18]:
# open zip code file
import json
with open('zip.geojson') as f:
data = json.load(f)
In [19]:
# Get shapfile zipcode list
zcta_zipcodes = []
for i in range(len(data['features'])):
zcta_zipcodes.append(str(data['features'][i]['properties']['ZCTA5CE10']))
In [20]:
# Get the zipcode, which only exists in ZCTA
zipcodes_filtered = []
for zipcode in df_city_zip.zipcodes:
if zipcode in zcta_zipcodes:
zipcodes_filtered.append(zipcode)
In [21]:
"""
'data' has a key 'feature' which has n rows,
each of which has 'properties' key which has 'ZCTA5CE10'
"""
df_feat = pd.DataFrame(data['features'])
In [22]:
df_feat.head()
Out[22]:
geometry
properties
type
0
{u'type': u'Polygon', u'coordinates': [[[-87.3...
{u'AWATER10': 12694318, u'AFFGEOID10': u'86000...
Feature
1
{u'type': u'Polygon', u'coordinates': [[[-110....
{u'AWATER10': 488785, u'AFFGEOID10': u'8600000...
Feature
2
{u'type': u'Polygon', u'coordinates': [[[-109....
{u'AWATER10': 3082, u'AFFGEOID10': u'8600000US...
Feature
3
{u'type': u'Polygon', u'coordinates': [[[-90.8...
{u'AWATER10': 10549055, u'AFFGEOID10': u'86000...
Feature
4
{u'type': u'Polygon', u'coordinates': [[[-118....
{u'AWATER10': 1540929, u'AFFGEOID10': u'860000...
Feature
In [23]:
# create a new df (temp) for selected cities
temp = pd.DataFrame()
for i in range(len(data['features'])):
if str(data['features'][i]['properties']['ZCTA5CE10']) in zipcodes_filtered:
temp = pd.concat([temp,df_feat.iloc[[i]]],ignore_index=True)
len(temp)
Out[23]:
586
In [24]:
temp.head()
Out[24]:
geometry
properties
type
0
{u'type': u'Polygon', u'coordinates': [[[-122....
{u'AWATER10': 1299131, u'AFFGEOID10': u'860000...
Feature
1
{u'type': u'Polygon', u'coordinates': [[[-77.0...
{u'AWATER10': 0, u'AFFGEOID10': u'8600000US205...
Feature
2
{u'type': u'Polygon', u'coordinates': [[[-73.9...
{u'AWATER10': 0, u'AFFGEOID10': u'8600000US112...
Feature
3
{u'type': u'Polygon', u'coordinates': [[[-74.0...
{u'AWATER10': 0, u'AFFGEOID10': u'8600000US102...
Feature
4
{u'type': u'Polygon', u'coordinates': [[[-73.9...
{u'AWATER10': 0, u'AFFGEOID10': u'8600000US101...
Feature
In [25]:
# compare the structure of the original data and the df so that we don't corrupt geojson structure
"""
Original
"""
from pprint import pprint
pprint(data['features'][0])
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u'type': u'Polygon'},
u'properties': {u'AFFGEOID10': u'8600000US36426',
u'ALAND10': 1258584220,
u'AWATER10': 12694318,
u'GEOID10': u'36426',
u'ZCTA5CE10': u'36426'},
u'type': u'Feature'}
In [26]:
"""
Trimmed:
"""
# same as the original
dict_temp = temp.to_dict(orient='records')
pprint(dict_temp[0])
{u'geometry': {u'coordinates': [[[-122.50808771796498,
37.73519779053799,
0.0],
[-122.502558, 37.735557, 0.0],
[-122.496449, 37.733877, 0.0],
[-122.493784, 37.733989, 0.0],
[-122.491294, 37.734096, 0.0],
[-122.49146, 37.737283, 0.0],
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[-122.488518, 37.737002, 0.0],
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[-122.464294, 37.722114, 0.0],
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[-122.460858, 37.710586, 0.0],
[-122.465994, 37.710154, 0.0],
[-122.468939, 37.708232, 0.0],
[-122.469236, 37.708232, 0.0],
[-122.471485, 37.708331, 0.0],
[-122.498302, 37.708132, 0.0],
[-122.498161, 37.710091, 0.0],
[-122.498442, 37.714886, 0.0],
[-122.499906, 37.717286, 0.0],
[-122.500403, 37.720144, 0.0],
[-122.502374, 37.722898, 0.0],
[-122.503128, 37.725218, 0.0],
[-122.50532, 37.726322, 0.0],
[-122.50705748535499,
37.72783608474219,
0.0],
[-122.50808771796498,
37.73519779053799,
0.0]]],
u'type': u'Polygon'},
u'properties': {u'AFFGEOID10': u'8600000US94132',
u'ALAND10': 8054467,
u'AWATER10': 1299131,
u'GEOID10': u'94132',
u'ZCTA5CE10': u'94132'},
u'type': u'Feature'}
In [27]:
# replace the original
data['features'] = dict_temp
In [28]:
# save as a new file
import json
with open('zip_cities.json', 'w') as f:
json.dump(data, f)
In [29]:
# test using folium
m = folium.Map(location=[37.769959, -122.448679], zoom_start=9)
geo_path = 'zip_cities.json'
m.geo_json(geo_path= geo_path)
m.create_map('zip_cities.html')
In [31]:
# save the zip code file
import pickle
with open('zipcode_final.txt', 'wb') as f:
pickle.dump(zipcodes_filtered, f)
In [ ]:
# # load the file
# with open('zipcode_final.txt', 'rb') as f:
# zip_list = pickle.load(f)
In [ ]:
Content source: hongsups/insightfl_shin
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