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CIFTools.py
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# python packages
from dataclasses import dataclass
from typing import Union, List
import requests
import urllib
import asyncio
import re
import functools
from functools import partial
from io import BytesIO
from zipfile import ZipFile
from glob import glob
from typing import Union, List
from os import getcwd, remove
import os
from csv import DictReader
from itertools import product
# for ascync requests
from aiohttp import ClientSession
# cfg
from CIF_Config import ACSConfig, SocrataConfig
# pandas / numpy
import pandas as pd
import numpy as np
# multi-processing
from joblib import Parallel, delayed
# import stateDF from utils
from utils import stateDf
# import beautifulSoup
from bs4 import BeautifulSoup
# Hell World from VIM
def batchify_variables(config: ACSConfig):
if config.acs_type == '':
source = 'acs/acs5'
else:
source = f'acs/acs5/{config.acs_type}'
table = config.variables
batch_size = 49
if len(table) > batch_size:
num_full = len(table)//batch_size
table_split = [table[k*batch_size:(k+1)*batch_size] for k in range(num_full)]
table_split.append(table[num_full*batch_size:])
return table_split
else:
return [table]
async def donwload_for_batch(config, table: str, key: str, session: ClientSession) -> List[str]:
if config.acs_type == '':
source = 'acs/acs5'
else:
source = f'acs/acs5/{config.acs_type}'
# table = ','.join(batchify_variables(config)[0])
if isinstance(config.state_fips, str) or isinstance(config.state_fips, int):
if config.query_level == 'state':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get={table}&for=state:{config.state_fips}&key={key}'
elif config.query_level == 'county':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get=NAME,{table}&for=county:*&in=state:{config.state_fips}&key={key}'
elif config.query_level == 'county subdivision':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get=NAME,{table}&for=county%20subdivision:*&in=state:{config.state_fips}&in=county:*&key={key}'
elif config.query_level == 'tract':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get=NAME,{table}&for=tract:*&in=state:{config.state_fips}&in=county:*&key={key}'
elif config.query_level == 'block':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get=NAME,{table}&for=block%20group:*&in=state:{config.state_fips}&in=county:*&in=tract:*&key={key}'
elif config.query_level == 'zip':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get=NAME,{table}&for=zip%20code%20tabulation%20area:*&in=state:{config.state_fips}&key={key}'
else:
print('The region level is not found in the system')
elif isinstance(config.state_fips, list):
config.state_fips = [str(x) for x in config.state_fips]
states = ','.join(config.state_fips)
if config.query_level == 'state':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get={table}&for=state:{states}&key={key}'
elif config.query_level == 'county':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get=NAME,{table}&for=county:*&in=state:{states}&key={key}'
elif config.query_level == 'county subdivision':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get=NAME,{table}&for=county%20subdivision:*&in=state:{states}&in=county:*&key={key}'
elif config.query_level == 'tract':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get=NAME,{table}&for=tract:*&in=state:{states}&in=county:*&key={key}'
elif config.query_level == 'block':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get=NAME,{table}&for=block%20group:*&in=state:{states}&in=county:*&in=tract:*&key={key}'
elif config.query_level == 'zip':
acs_url = f'https://api.census.gov/data/{config.year}/{source}?get=NAME,{table}&for=zip%20code%20tabulation%20area:*&in=state:{states}&key={key}'
else:
print('The region level is not found in the system')
resp = await session.request(method="GET", url=acs_url)
resp.raise_for_status()
json_raw = await resp.json()
return json_raw
async def download_all(config, key):
tables = batchify_variables(config)
async with ClientSession() as session:
tasks = [donwload_for_batch(config, f"{','.join(table)}", key, session) for table in tables]
return await asyncio.gather(*tasks)
def acs_data(key, config = None, **kwargs):
import sys
if config:
pass
else:
config = ACSConfig(**kwargs)
if sys.platform in ['win32','cygwin']:
# asyncio.set_event_loop_policy(asyncio.WindowsProactorEventLoopPolicy()) # not working
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
result = asyncio.get_event_loop().run_until_complete(download_all(config, key))
else:
result = asyncio.run(download_all(config, key))
if len(result) == 1:
df = pd.DataFrame(result[0][1:], columns = result[0][0])
data_columns = df.columns[df.columns.str.contains(config.acs_group)].tolist()
index_columns = df.columns[~df.columns.str.contains(config.acs_group)].tolist()
df = df[index_columns + data_columns]
else:
for i, res in enumerate(result):
if i == 0:
df = pd.DataFrame(res[1:], columns = res[0])
else:
df_1 = pd.DataFrame(res[1:], columns = res[0])
df = df.merge(df_1, how = 'inner', on = df.columns[df.columns.str.isalpha()].tolist())
df = pd.concat([df.loc[:, df.columns.str.isalpha()], df.loc[:, ~df.columns.str.isalpha()]], axis = 1)
return df
def custom_acs_data(key, config = None, **kwargs):
def decorator_transform(func, key = key, config = config):
if config:
pass
else:
config = ACSConfig(**kwargs)
@functools.wraps(func)
def wrapper(**kwargs):
df = acs_data(key, config)
df = func(df)
return df
return wrapper
return decorator_transform
###################################################################################################################
####################### census sdoh variables for CIFTools ########################################################
###################################################################################################################
@dataclass
class acs_sdoh:
year: int
state_fips: Union[str, int]
query_level: str
key: str
def cancer_infocus_download(self):
cancer_infocus_funcs = [self.__getattribute__(x) for x in self.__dir__() if re.match('gen_\w.+_table', x)]
for func in cancer_infocus_funcs:
func()
output = self.download_all()
return output
def download_all(self):
res = Parallel(n_jobs=-1)(delayed(fun)() for fun in self.functions.values())
res = Parallel(n_jobs=-1)(delayed(self.cleanup_geo_col)(df, self.query_level) for df in res)
return {key:val for key, val in zip(self.functions.keys(), res)}
def clean_functions(self):
if hasattr(self, 'functions'):
self.functions = {}
else:
pass
@staticmethod
def cleanup_geo_col(df, level):
if level in ['county subdivision','tract','block', 'county']:
name_series = df.NAME.str.split(', ')
county_name = [x[-2] for x in name_series]
state_name = [x[-1] for x in name_series]
FIPS = df[df.columns[df.columns.isin(
['county subdivision','tract','block', 'county', 'state'])]].apply(lambda x: ''.join(x), axis = 1)
if level != "county":
subgroup_name = [x[0] for x in name_series]
columns = pd.DataFrame(zip(FIPS, subgroup_name, county_name, state_name),
columns = ['FIPS', level.title(), 'County','State'])
else:
columns = pd.DataFrame(zip(FIPS, county_name, state_name), columns = ['FIPS','County','State'])
elif level == 'zip':
zip_name = [x[-5:] for x in df.NAME]
states = {x:stateDf.loc[stateDf.FIPS2.eq(str(x)),'State'].values[0] for x in df.state.unique()}
state_name = df.state.apply(lambda x: states[x])
columns = pd.DataFrame(zip(zip_name, state_name), columns = ['ZCTA5','State'])
geo_name = ['county subdivision','tract','block', 'county', 'zip', 'state']
df = df.drop(df.columns[df.columns.isin(geo_name)].tolist() + ['NAME'], axis = 1)
df = pd.concat([columns, df], axis = 1)
df = df.sort_values('FIPS').reset_index(drop = True)
return df
def add_function(self, func, name):
if hasattr(self, "functions"):
pass
else:
self.functions = {}
self.functions[name] = func
def gen_acs_config(self, **kwargs):
arguements = {'year': self.year,
'state_fips': self.state_fips,
'query_level': self.query_level,
'acs_group': kwargs['acs_group'],
'acs_type': kwargs['acs_type']}
self.config = ACSConfig(**arguements)
return self.config
#####################################################################
# Check this out!!!!!!!!!!!!!
#####################################################################
def add_custom_table(self, group_id, acs_type, name):
config = self.gen_acs_config(**{'acs_group': group_id, 'acs_type': acs_type})
def transform_data(func):
@functools.wraps(func)
def wrapper(**kwargs):
df = acs_data(self.key, config)
df = func(df, **kwargs)
return df
self.add_function(wrapper, name)
return wrapper
return transform_data
def gen_insurance_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': ["B27001", "C27007",], 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
insurance_col = [config.variables[config.labels.index(x)] for x in config.labels if re.match('.+With health insurance coverage', x)]
medicaid_col = [config.variables[config.labels.index(x)] for x in config.labels if re.match('.+With Medicaid/means-tested public coverage', x)]
df['health_insurance_coverage_rate'] = df.loc[:, insurance_col].astype(int).sum(axis = 1)/df.B27001_001E.astype(int)
df['medicaid'] = df.loc[:,medicaid_col].astype(int).sum(axis = 1)/df.C27007_001E.astype(int)
df.drop(config.variables, axis = 1, inplace = True)
return df
self.add_function(transform_df, 'insurance')
if return_table:
return transform_df()
def gen_vacancy_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': 'B25002', 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
df['vacancy_rate'] = df.B25002_003E.astype(int)/df.B25002_001E.astype(int)
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'vacancy')
if return_table:
return transform_df()
def gen_poverty_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': 'B17026', 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
df['below_poverty_x.5'] = df.B17026_002E.astype(int)/df.B17026_001E.astype(int)
df['below_poverty'] = df.loc[:, df.columns.str.match(re.compile('B17026_00[2-4]E'))].astype(int).sum(axis = 1).astype(int)/df.B17026_001E.astype(int)
df['below_poverty_x2'] = (df.B17026_010E.astype(int) + df.loc[:, df.columns.str.match(re.compile('B17026_00[2-9]E'))].astype(int).sum(axis = 1)).astype(int)/df.B17026_001E.astype(int)
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'poverty')
if return_table:
return transform_df()
def gen_transportation_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': 'B08141', 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
df['no_vehicle'] = df.B08141_002E.astype(int)/df.B08141_001E.astype(int)
df['two_or_more_vehicle'] = (df.B08141_004E.astype(int) + df.B08141_005E.astype(int))/df.B08141_001E.astype(int)
df['three_or_more_vehicle'] = df.B08141_005E.astype(int)/df.B08141_001E.astype(int)
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'transportation')
if return_table:
return transform_df()
def gen_employment_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': 'B23025', 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
df['Labor Force Participation Rate'] = df.B23025_003E.astype(
int)/(df.B23025_003E.astype(int) + df.B23025_007E.astype(int))
df['Unemployment Rate'] = df.B23025_005E.astype(int)/df.B23025_003E.astype(int)
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'employment')
if return_table:
return transform_df()
def gen_gini_index_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': 'B19083', 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
df = df.rename(columns = {'B19083_001E': 'Gini Index'})
df['Gini Index'] = df['Gini Index'].astype(float).apply(lambda x: x if x>=0 else np.nan)
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'gini_index')
if return_table:
return transform_df()
def gen_rent_to_income_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': 'B25070', 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
df['rent_over_40'] = (df.B25070_009E.astype(int) + df.B25070_010E.astype(int))/df.B25070_001E.astype(int)
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'rent_to_income')
if return_table:
return transform_df()
def gen_old_house_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': 'B25034', 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
df['houses_before_1960'] = df[['B25034_009E','B25034_010E','B25034_011E']].astype(int).sum(axis = 1)/df.B25034_001E.astype(int) ##### DOUBLE CHECK
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'houses_before_1960')
if return_table:
return transform_df()
def gen_public_assistance_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': 'B19058', 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
df['public_assistance_recieved'] = df.B19058_002E.astype(int)/df.B19058_001E.astype(int)
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'public_assistance')
if return_table:
return transform_df()
def gen_education_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': 'B15003', 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
# col1 is for below 9th grade
col1_label = ['Nursery school', 'No schooling completed', '5th grade', '3rd grade', '4th grade', '2nd grade', '1st grade', 'Kindergarten','8th grade', '7th grade', '6th grade']
col1 = [config.variables[config.labels.index(x)] for x in col1_label]
# col4 is for advanced degree
col4_label = ['Doctorate degree','Professional school degree', "Master's degree"]
col4 = [config.variables[config.labels.index(x)] for x in col4_label]
# col3 is for 4 years college and above: (it changes at the end, but for now, it includes any college to define col2, which is high school and above)
col3_label = ["Bachelor's degree", "Associate's degree", "Some college, 1 or more years, no degree",'Some college, less than 1 year'] + col4_label
col3 = [config.variables[config.labels.index(x)] for x in col3_label]
# col2 is high school and above
col2_label = ['Regular high school diploma'] + col3_label + col4_label
col2 = [config.variables[config.labels.index(x)] for x in col2_label]
# col5 is for completed college
col5_label = ["Bachelor's degree"] + col4_label
col5 = [config.variables[config.labels.index(x)] for x in col5_label]
df['Total'] = df.B15003_001E.astype(int)
df['Below 9th grade'] = df.loc[:, col1].astype(int).sum(axis = 1)/df.Total
df['High School'] = df.loc[:, col2].astype(int).sum(axis = 1)/df.Total
df['College'] = df.loc[:, col5].astype(int).sum(axis = 1)/df.Total
df['Advanced Degree'] = df.loc[:, col4].astype(int).sum(axis = 1)/df.Total
df.drop('Total', axis = 1, inplace = True)
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'education')
if return_table:
return transform_df()
def gen_income_table(self, group_id = "B19013", race = 'all', return_table = False):
config = self.gen_acs_config(**{'acs_group': group_id, 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
df[f'median_income_{race}'] = df[f'{config.acs_group}_001E'].astype(float)
df.loc[df[f'median_income_{race}'].le(0), f'median_income_{race}'] = np.nan
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
if race == 'all':
self.add_function(transform_df, "income")
else:
self.add_function(transform_df, f"income_{race}")
if return_table:
return transform_df()
def gen_age_demographic_table(self, group_id = "B01001", age_groups: Union[str, dict] = '18-64', return_table = False):
config = self.gen_acs_config(**{'acs_group': group_id, 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
if isinstance(age_groups, str):
if age_groups in ['ten years', '18-64']:
if age_groups == 'ten years':
age_group_dict = ten_year_age_groups(config = config) # you can chage the age group defnition here
else:
age_group_dict = large_age_groups(config = config) # you can chage the age group defnition here
else:
raise ValueError("you should choose between 'ten years' or '18-64'; otherwise, provide a custom age_groups in a dictionary format")
elif isinstance(age_groups, dict):
try:
age_group_dict = find_index_for_age_group(age_groups, config = config)
except:
raise ValueError("Please follow the guideline for the custom age_groups")
df['Total'] = df[group_id + "_001E"].astype(int)
for key, val in age_group_dict.items():
col = [x for x in config.variables if config.variables.index(x) in val]
df[key] = df.loc[:, col].astype(int).apply(np.sum, axis = 1)/df['Total']
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'demographic_age')
if return_table:
return transform_df()
def gen_race_demographic_table(self, return_table = False):
config = self.gen_acs_config(**{'acs_group': 'B03002', 'acs_type': ''})
@custom_acs_data(self.key, config)
def transform_df(df):
def gen_race_series(race_variable, df = df, config = config):
newdf = df.copy()
race_series = newdf[race_variable].astype(int)/newdf.B03002_001E.astype(int)
del newdf
return race_series
variables = ['B03002_' + x for x in ['003E','004E','012E','006E']]
race_names = ['White','Black','Hispanic','Asian']
for v, n in zip(variables, race_names):
df[n] = gen_race_series(v)
df['Other_Races'] = (df.B03002_002E.astype(int) - df.loc[:,['B03002_003E','B03002_004E','B03002_006E']].astype(int).sum(1))/df.B03002_001E.astype(int)
df.drop(df.columns[df.columns.str.contains(config.acs_group)], axis = 1, inplace = True)
return df
self.add_function(transform_df, 'demographic_race')
if return_table:
return transform_df()
#########################
# utils for Demographics
#########################
ten_years = dict(zip(['Under 5 years', '5 to 14 years','15 to 24 years',
'25 to 34 years','35 to 44 years','45 to 54 years',
'55 to 64 years','65 to 74 years','75 to 84 years',
'85 years and over'],
[(0, 4), (5, 14), (15, 24), (25, 34), (35, 44), (45, 54), (55, 64),
(65, 74), (75, 84), (85, 100)]))
total_years = dict(zip(['Under 18', '18 to 64', 'Over 64'],
[(0, 17), (18, 64), (65, 100)]))
def find_index_for_age_group(age_group_dict, config = None, **kwargs):
if config:
pass
else:
config = ACSConfig(**kwargs)
def extract_age_range(text):
"""from the labels for B01001, this extracts age interval in a tuple:
\d year old -> (\d, \d)
Under \d years -> (0, \d)
\d_1 to \d_2 -> (\d_1, \d_2)
\d and over -> (\d, 100)
"""
def check_integer(s):
try:
int(s)
return True
except:
return False
numbers = [int(x) for x in text.split(' ') if check_integer(x)]
if len(numbers) == 1:
numbers = numbers + numbers
return tuple(numbers)
one = [[x.replace('Under', '0 to').replace('over','100'), config.labels.index(x)] for x in config.labels if re.match('.*years.*', x)]
two = [(extract_age_range(x[0]), x[1]) for x in one]
def check_in_between(t1, t2):
if t1[0] >= t2[0] and t1[1] <= t2[1]:
return True
else:
return False
def find_age_group(test):
for k, v in age_group_dict.items():
if check_in_between(test[0], v):
return k
# def find_index_for_age_group(new_def, two):
index_by_age_group = {k: [] for k in age_group_dict.keys()}
for t in two:
index_by_age_group[find_age_group(t)].append(t[1])
return index_by_age_group
large_age_groups = partial(find_index_for_age_group, age_group_dict = total_years)
ten_year_age_groups = partial(find_index_for_age_group, age_group_dict = ten_years)
###################################################################
###################################################################
# facilities ###################################################
###################################################################
###################################################################
def gen_facility_data(location:Union[List[str], str], taxonomy:List[str] = ['Gastroenterology','colon','obstetrics']):
data_dict = {}
data_dict['nppes'] = nppes(location)
functions = [mammography, hpsa, fqhc, lung_cancer_screening, toxRel_data] #, superfund]
dataset_names = ['mammography', 'hpsa','fqhc','lung_cancer_screening', 'tri_facility'] #, 'superfund_site']
datasets = Parallel(n_jobs=-1)(delayed(f)(location) for f in functions)
for name, df in zip(dataset_names, datasets):
data_dict[name] = df
return data_dict
###################################################################
## toxRel_data
###################################################################
def toxRel_data(location:Union[str, List[str]]):
from tqdm import tqdm
import datetime
import os
today = datetime.date.today(); year = today.year
flag = True
while flag: # this while statement will look for the most recent tri data
resp = requests.get(f'https://data.epa.gov/efservice/downloads/tri/mv_tri_basic_download/{year}_US/csv', stream=True)
try:
resp.raise_for_status()
flag = False
except:
year -= 1
total = int(resp.headers.get('content-length', 0))
fname = os.path.join(getcwd(), 'toxRel.csv')
chunk_size = int(1024*1024/2)
with open(fname, 'wb') as file, tqdm(
desc="downloading toxRel data file",
total=total,
unit='iB',
unit_scale=True,
unit_divisor=1024,
leave = True
) as bar:
for data in resp.iter_content(chunk_size=chunk_size):
size = file.write(data)
bar.update(size)
with open(fname, newline='') as csvfile:
reader = DictReader(csvfile)
############
colnames = ['FRS ID', 'FACILITY NAME', # 'STREET ADDRESS','CITY','ST','ZIP',
'LATITUDE', 'LONGITUDE', 'COUNTY', 'CHEMICAL', 'ST']
csv_keys = [field for col, field in product(colnames, reader.fieldnames) if \
re.match("\d+\.\s" + col + "$", field, flags = re.I) ]
temp_col = ['STREET ADDRESS','CITY','ST', 'ZIP', 'CARCINOGEN']
temp_field = [field for col, field in product(temp_col, reader.fieldnames) if \
re.match("\d+\.\s" + col + "$", field, flags = re.I) ]
temp = dict(zip(temp_col, temp_field))
# csv_keys = ['3. FRS ID', '4. FACILITY NAME', #. '5. STREET ADDRESS', '6. CITY', '8. ST', '9. ZIP',
# '12. LATITUDE', '13. LONGITUDE', '7. COUNTY', '34. CHEMICAL', '8. ST']
data_dict = dict(zip(colnames, [[] for _ in range(len(colnames))]))
data_dict['Address'] = []
if isinstance(location, str):
assert len(location) == 2
for row in reader:
if (row[temp['ST']] == location.upper()) & (
row[temp['CARCINOGEN']] == 'YES'):
address = row[temp['STREET ADDRESS']].title() + ', ' + row[temp['CITY']].title() + ', ' + row[temp['ST']].upper() + ' ' + str(row[temp['ZIP']])
data_dict['Address'].append(address)
for dict_key, row_key in zip(colnames, csv_keys):
data_dict[dict_key].append(row[row_key])
else:
for loc in location:
assert len(loc) == 2
for row in reader:
if (row[temp['ST']] in [x.upper() for x in location]) & (
row[temp['CARCINOGEN']] == 'YES'):
address = row[temp['STREET ADDRESS']].title() + ', ' + row[temp['CITY']].title() + ', ' + row[temp['ST']].upper() + ' ' + str(row[temp['ZIP']])
data_dict['Address'].append(address)
for dict_key, row_key in zip(colnames, csv_keys):
data_dict[dict_key].append(row[row_key])
df = pd.DataFrame(data_dict)
remove(fname)
del data_dict
df = df.groupby(["FRS ID", 'FACILITY NAME',
'Address', 'LATITUDE',
'LONGITUDE', 'COUNTY', 'ST'])['CHEMICAL'].agg(lambda col: ', '.join(col)).reset_index()
df['Notes'] = 'Chemicals relased: ' + df['CHEMICAL']
df = df[['FACILITY NAME', 'Address', 'LATITUDE', 'LONGITUDE', 'COUNTY', 'ST', 'Notes']]
df = df.rename(columns = {'FACILITY NAME': 'Name', 'LATITUDE': 'latitude', 'LONGITUDE': 'longitude',
'ST':'State'})
df['Type'] = 'Toxic Release Inventory Facility'
df['Phone_number'] = None
return df[['Type', 'Name', 'Address', 'State', 'Phone_number', 'Notes', 'latitude', 'longitude']] # You can add FIPS5 if you find COUNTY from a county-> fipscode map
###################################################################
## superfund
###################################################################
def gen_single_superfund(location: str):
assert len(location) == 2; assert location.isalpha()
url = f'https://data.epa.gov/efservice/SEMS_ACTIVE_SITES/SITE_STATE/{location}/JSON'
sf = pd.read_json(url, dtype = {'SITE_FIPS_CODE':str, 'SITE_ZIP_CODE':str})
sf.SITE_FIPS_CODE = sf.SITE_FIPS_CODE.apply(lambda x: x[:5])
sf.SITE_ZIP_CODE = sf.SITE_ZIP_CODE.apply(lambda x: x[:5])
sf2 = sf.loc[sf.NPL.isin(['Currently on the Final NPL', 'Deleted from the Final NPL'])]
sf3 = sf2[['SITE_NAME', 'SITE_STRT_ADRS1', 'SITE_CITY_NAME', 'SITE_STATE', 'SITE_ZIP_CODE',
'SITE_FIPS_CODE', 'NPL', 'LATITUDE', 'LONGITUDE']]
sf3 = sf3.assign(Address = sf3['SITE_STRT_ADRS1'] + ', ' + sf3['SITE_CITY_NAME'] + ', ' + sf3['SITE_STATE'] + ' ' + sf3['SITE_ZIP_CODE'].astype(str))
sf3 = sf3.rename(columns = {'SITE_NAME':'Name', 'SITE_FIPS_CODE':'FIPS', 'NPL':'Notes',
'LATITUDE':'latitude', 'LONGITUDE':'longitude',
'SITE_STATE':'State'})
sf3.drop(['SITE_STRT_ADRS1', 'SITE_CITY_NAME', 'SITE_ZIP_CODE'], axis=1, inplace=True)
sf3['Type'] = 'Superfund Site'
sf3['Phone_number'] = None
del sf, sf2
return sf3[['Type', 'Name', 'Address', 'State', 'Phone_number', 'Notes', 'latitude', 'longitude', 'FIPS']]
def superfund(location: Union[str, List[str]]):
if isinstance(location, str):
if location.isnumeric():
location = stateDf.loc[stateDf.FIPS2.eq(location),'StateAbbrev'].values[0]
try:
df = gen_single_superfund(location)
df = df.reset_index(drop = True)
except:
print(f'\nsuperfund data for {location} is not available\n')
df = None
else:
if any([x.isnumeric() for x in location]):
for i, loc in enumerate(location):
if loc.isnumeric():
location[i] = stateDf.loc[stateDf.FIPS2.eq(loc),'StateAbbrev'].values[0]
datasets = []
for loc in location:
try:
datasets.append(gen_single_superfund(loc))
except:
print(f"superfund data for {loc} is not available")
pass
df = pd.concat(datasets, axis = 0).reset_index(drop = True)
return df
###################################################################
## mammography
###################################################################
def mammography(location: Union[str, List[str]]):
url = urllib.request.urlopen("http://www.accessdata.fda.gov/premarket/ftparea/public.zip")
with ZipFile(BytesIO(url.read())) as my_zip_file:
df = pd.DataFrame(my_zip_file.open(my_zip_file.namelist()[0]).readlines(), columns= ['main'])
df = df.main.astype(str).str.split('|', expand = True)
df.columns = ['Name','Street','Street2','Street3','City','State','Zip_code','Phone_number', 'Fax']
# state = state.upper()
if isinstance(location, str):
df = df.loc[df.State.eq(location)].reset_index(drop = True)
else:
df = df.loc[df.State.isin(location)].reset_index(drop = True)
df.Name = df.Name.str.extract(re.compile('[bB].(.*)'))
df['Address'] = df['Street'] + ', ' + df['City'] + ', ' + df['State'] + ' ' + df['Zip_code']
df['Type'] = 'Mammography'
df['Notes'] = ''
def convert_phone_number(match_obj):
first = '(' + match_obj.group(1)[:3] + ") " + match_obj.group(1)[3:]
if len(match_obj.group(2)) > 4:
second = match_obj.group(2)[:4] + " ext. " + match_obj.group(2)[4:]
else:
second = match_obj.group(2)
return first + '-' + second
df['Phone_number'] = df.Phone_number.str.replace("(\d+)-(\d+)", convert_phone_number, regex =True)
return df.loc[:,['Type','Name','Address','State', 'Phone_number', 'Notes']] #try to add FIPS and State
###################################################################
## hpsa
###################################################################
def download_hpsa_data():
from tqdm import tqdm
import os
resp = requests.get('https://data.hrsa.gov/DataDownload/DD_Files/BCD_HPSA_FCT_DET_PC.xlsx', stream=True)
total = int(resp.headers.get('content-length', 0))
fname = os.path.join(getcwd(), 'hpsa.xlsx')
chunk_size = 1024*10
with open(fname, 'wb') as file, tqdm(
desc="downloading hpsa data file",
total=total,
unit='iB',
unit_scale=True,
unit_divisor=1024,
leave = True
) as bar:
for data in resp.iter_content(chunk_size=chunk_size):
size = file.write(data)
bar.update(size)
output = pd.read_excel(fname, engine = 'openpyxl')
remove(fname)
return output
def hpsa(location: Union[str, List[str]]):
df= download_hpsa_data()
df.columns = df.columns.str.replace(' ','_')
if isinstance(location, str):
df = df.loc[df.Primary_State_Abbreviation.eq(location)&
df.HPSA_Status.eq('Designated')&
df.Designation_Type.ne('Federally Qualified Health Center')].reset_index(drop = True)
else:
df = df.loc[df.Primary_State_Abbreviation.isin(location)&
df.HPSA_Status.eq('Designated')&
df.Designation_Type.ne('Federally Qualified Health Center')].reset_index(drop = True)
df = df[['HPSA_Name','HPSA_ID','Designation_Type','HPSA_Score','HPSA_Address',
'HPSA_City', 'State_Abbreviation', 'Common_State_County_FIPS_Code',
'HPSA_Postal_Code','Longitude','Latitude']]
pattern = re.compile('(\d+)-\d+')
df['HPSA_Postal_Code'] = df.HPSA_Postal_Code.str.extract(pattern)
df['HPSA_Street'] = df['HPSA_Address'] + ', ' + df['HPSA_City'] + \
', ' + df['State_Abbreviation'] + ' ' + df['HPSA_Postal_Code']
df = df.drop_duplicates()
df['Type'] = 'HPSA '+df.Designation_Type
df = df.rename(columns = {'HPSA_Name' : 'Name', 'HPSA_Street':'Address',
'Common_State_County_FIPS_Code': 'FIPS', 'State_Abbreviation': 'State',
'Longitude': 'longitude', 'Latitude': 'latitude'})
df = df[['Type','Name','HPSA_ID','Designation_Type','HPSA_Score','Address',
'FIPS', 'State', 'longitude','latitude']]
df = df.loc[df.longitude.notnull()|df.Address.notnull()].reset_index(drop = True)
df['Phone_number'] = None
df['Notes'] = ''
return df[['Type','Name', 'Address', 'State', 'Phone_number', 'Notes', 'latitude', 'longitude']] #try to add FIPS
###################################################################
## fqhc
###################################################################
def download_fqhc_data(location: Union[str, List[str]]):
from tqdm import tqdm
import os
resp = requests.get('https://data.hrsa.gov//DataDownload/DD_Files/Health_Center_Service_Delivery_and_LookAlike_Sites.csv', stream = True)
total = int(resp.headers.get('content-length', 0))
fname = os.path.join(getcwd(), 'fqhc.csv')
chunk_size = 1024*10
with open(fname, 'wb') as file, tqdm(
desc="downloading fqhc data file",
total=total,
unit='iB',
unit_scale=True,
unit_divisor=1024,
leave = True
) as bar:
for data in resp.iter_content(chunk_size=chunk_size):
size = file.write(data)
bar.update(size)
with open(fname, newline='') as csvfile:
reader = DictReader(csvfile)
colnames = ['Health_Center_Type', 'Site_Name','Site_Address','Site_City','Site_State_Abbreviation',
'Site_Postal_Code','Site_Telephone_Number',
'Health_Center_Service_Delivery_Site_Location_Setting_Description',
'Geocoding_Artifact_Address_Primary_X_Coordinate',
'Geocoding_Artifact_Address_Primary_Y_Coordinate']
csv_keys = [x.replace('_', ' ') for x in colnames]
data_dict = dict(zip(colnames, [[] for _ in range(len(colnames))]))
for row in reader:
if isinstance(location, str):
if (row['Site State Abbreviation'] == location.upper()) & (
row['Health Center Type'] == 'Federally Qualified Health Center (FQHC)') & (
row['Site Status Description'] == 'Active'):
for dict_key, row_key in zip(colnames, csv_keys):
data_dict[dict_key].append(row[row_key])
else:
if (row['Site State Abbreviation'] in [x.upper() for x in location]) & (
row['Health Center Type'] == 'Federally Qualified Health Center (FQHC)') & (
row['Site Status Description'] == 'Active'):
for dict_key, row_key in zip(colnames, csv_keys):
data_dict[dict_key].append(row[row_key])
output = pd.DataFrame(data_dict)
del data_dict
remove(fname)
return output
def fqhc(location: Union[str, List[str]]):
df= download_fqhc_data(location)
df['Type'] = 'FQHC'
df['Address'] = df['Site_Address'] + ', ' + df['Site_City'] + ', ' + \
df['Site_State_Abbreviation'] + ' ' + df['Site_Postal_Code']
df = df.rename(columns = {'Site_Name':'Name', 'Site_Telephone_Number': 'Phone_number',
'Site_State_Abbreviation': 'State',
'Health_Center_Service_Delivery_Site_Location_Setting_Description': 'Notes',
'Geocoding_Artifact_Address_Primary_X_Coordinate': 'longitude',
'Geocoding_Artifact_Address_Primary_Y_Coordinate': 'latitude'})
df = df.loc[df.Address.notnull()].reset_index(drop = True)
return df[['Type', 'Name', 'Address', 'State','Phone_number', 'Notes', 'latitude', 'longitude']]
###################################################################
## nppes
###################################################################
def parse_basic(basic):
if 'organization_name' in basic.keys():
name = basic['organization_name'].title()
else:
if 'middle_name' in basic.keys():
name = basic['first_name'].title() + ' ' + basic['middle_name'][0].upper() + ' ' + basic['last_name'].title()
else:
name = basic['first_name'].title() + ' ' + basic['last_name'].title()
if 'credential' in basic.keys():
name = name + ' ' + basic['credential'].upper()
return name
def parse_address(address):
address_dict = [x for x in address if x['address_purpose'] == 'LOCATION'][0]
if 'address_2' in address_dict.keys():
street = address_dict['address_1'].title() + ', ' + address_dict['address_2'].title() + ', ' + address_dict['city'].title() + ', ' + address_dict['state'].upper()
else:
street = address_dict['address_1'].title() + ', ' + address_dict['city'].title() + ', ' + address_dict['state'].upper()
if 'postal_code' in address_dict.keys():
street += ' '
street += address_dict['postal_code'][:5]
if 'telephone_number' in address_dict.keys():
phone_number = address_dict['telephone_number']
else:
phone_number = None
state = address_dict['state']
return street, phone_number, state
taxonomy = ['Gastroenterology','colon','obstetrics']
taxonomy_names = dict(zip(taxonomy, ['Gastroenterology','Colon & Rectal Surgeon','Obstetrics & Gynecology']))
def gen_nppes_by_taxonomy(taxonomy: str, location: str):
count = 0
result_count = 200
skip = 0
datasets = []
while result_count == 200:
count += 1
url = f'https://npiregistry.cms.hhs.gov/api/?version=2.1&address_purpose=LOCATION&number=&state={location}&taxonomy_description={taxonomy}&skip={200*(count -1)}&limit=200'
resp = requests.get(url)
output = resp.json()
if 'result_count' in output.keys():
result_count = output['result_count']
if result_count:
df = pd.DataFrame(output['results'])
df['Name'] = df.basic.apply(parse_basic)
df['Phone_number'] = df.addresses.apply(lambda x: parse_address(x)[1])
df['Address'] = df.addresses.apply(lambda x: parse_address(x)[0])
df['State'] = df.addresses.apply(lambda x: parse_address(x)[2])
df = df.loc[df.State.eq(location), :].reset_index(drop = True)
if taxonomy in taxonomy_names.keys():
df['Type'] = taxonomy_names[taxonomy]
else:
df['Type'] = taxonomy
df['Notes'] = ['' if x[-5:].isnumeric() else 'missing zip code' for x in df.Address] # MA has one missing zip code
if result_count == 200: # if result_count is 200, it is very likely to have more data
df = df[['Type','Name','Address','State', 'Phone_number', 'Notes']]
if count % 7 == 0 : # sometimes, it returns the same datasets over and over
if (datasets[-1].shape[0]==200) & (df.shape[0] < 200):
result = pd.concat(datasets, axis = 0).reset_index(drop = True)
result = result.drop_duplicates()
return result
elif datasets[-1].shape[0] == df.shape[0]:
result = pd.concat(datasets, axis = 0).reset_index(drop = True)
result = result.drop_duplicates()
return result
else:
datasets.append(df[['Type','Name','Address','State', 'Phone_number', 'Notes']])
elif count == 1:
if result_count:
return df[['Type','Name','Address','State', 'Phone_number', 'Notes']]
else:
df = pd.DataFrame(columns = ['Type','Name','Address','State', 'Phone_number', 'Notes'])
return df
else:
if result_count:
datasets.append(df[['Type','Name','Address','State', 'Phone_number', 'Notes']])
else:
result = pd.concat(datasets, axis = 0).reset_index(drop = True)
return result
else:
if len(datasets):
result = pd.concat(datasets, axis = 0).reset_index(drop = True)
result_count = 0
return result
else:
break
def nppes(location:Union[str, List[str]], taxonomy:List[str] = ['Gastroenterology','colon','obstetrics']) -> pd.DataFrame:
if isinstance(location, str):
res = Parallel(n_jobs=-1)(delayed(gen_nppes_by_taxonomy)(t, location) for t in taxonomy)
else:
from itertools import product
res = Parallel(n_jobs=-1)(delayed(gen_nppes_by_taxonomy)(t, loc) for t, loc in product(taxonomy, location))
print('Process is complete')
return pd.concat(res, axis = 0)
###################################################################
## lung_cancer_screening ########################################## -> multiprocessing with multiple states
###################################################################
def setup_chrome_driver():
import sys
import os
if sys.platform in ['win32','cygwin']: # if the platfor is windows
glob_result = glob(os.path.join(os.getcwd(), '*', 'chromedriver.exe'))
else: # if the platform is either linux or mac os
glob_result = glob(os.path.join(os.getcwd(), '*', 'chromedriver'))
if len(glob_result) == 0: # if chromedriver is not found
import chromedriver_autoinstaller
fp = chromedriver_autoinstaller.install('.') # install chromedriver_autoinstaller
else:
fp = glob_result[0]
return fp
def lung_cancer_screening_file_download(chrome_driver_path = None):
try: # first try requests
resp = requests.get('https://report.acr.org/t/PUBLIC/views/NRDRLCSLocator/ACRLCSDownload.csv', stream = True)
resp.raise_for_status()
total = int(resp.headers.get('content-length', 0))
chunk_size = 1024