def state_zip_by_popdense(state_str, num_returns):
search = SearchEngine()
res = search.query(state=state_str,
sort_by=Zipcode.population_density,
ascending=False,
returns=num_returns)
return [x.zipcode for x in res]
def get_zipcode_of_richest_near_city(lat, lng):
search = SearchEngine(simple_zipcode=True)
zip = []
radius = 25
res = search.query(lat=lat,
lng=lng,
radius=radius,
sort_by=Zipcode.median_household_income,
ascending=True,
returns=25)
for i in res:
zip.append(i.zipcode)
return zip
def get_zipcode_of_richest_near_city(lat, lng):
search = SearchEngine(simple_zipcode=True)
zip=[]
radius = 100
res = search.query(
lat=lat,
lng=lng,
radius=radius,
sort_by=Zipcode.median_household_income,
ascending=False,
returns=100,
)
print(res)
for i in res:
zip.append(str(i.median_household_income) + " " + i.major_city)
return zip
def get_city_data():
city_zip = []
for i in range(0, zip_dem.shape[0]):
search = SearchEngine(simple_zipcode=False)
res = search.query(city=zip_dem['major_city'][i],
state=zip_dem['state'][i],
returns=100)
for zipcode in res:
city_zip.append(zipcode.zipcode)
city_data = pd.DataFrame()
for i in city_zip:
zipcode = search.by_zipcode(i)
table = json_normalize(json.loads(zipcode.to_json()))
city_data = pd.concat([city_data, table], axis=0)
city_data = city_data.reset_index(drop=True)
city_data.dropna(inplace=True)
city_data = city_data.replace({None: np.nan})
city_data = city_data.reset_index(drop=True)
return city_data
