def get_hospitalizations(session):
"""Returns a DataFrame of per-facility hospitalization stats."""
cache_path = cached_path(session, f"{DATA_URL}:feather")
if cache_path.exists():
df = pandas.read_feather(cache_path)
else:
response = session.get(DATA_URL)
response.raise_for_status()
df = pandas.read_csv(
io.StringIO(response.text),
dtype={
"hospital_pk": str,
"fips_code": "Int64",
"ccn": str,
},
parse_dates=["collection_week"],
date_parser=lambda v: pandas.to_datetime(v, utc=True),
)
df.dropna(subset=["fips_code"], inplace=True)
df.reset_index(drop=True, inplace=True)
with temp_to_rename(cache_path) as temp_path:
df.to_feather(temp_path)
df.set_index(
["fips_code", "hospital_pk", "collection_week"],
drop=True,
inplace=True,
verify_integrity=True,
)
df.sort_index(inplace=True)
return df
def get_vaccinations(session):
"""Returns a DataFrame of county-level vaccination stats."""
cache_path = cached_path(session, f"{DATA_URL}:feather")
if cache_path.exists():
df = pandas.read_feather(cache_path)
else:
response = session.get(DATA_URL)
response.raise_for_status()
df = pandas.read_csv(
io.StringIO(response.text),
dtype={"FIPS": "Int64"},
na_values=["UNK"],
)
df = df[[
"Date",
"FIPS",
"Administered_Dose1_Recip",
"Series_Complete_Yes",
"Booster_Doses",
]]
df.dropna(subset=["FIPS"], inplace=True)
df.Date = pandas.to_datetime(df.Date, utc=True)
df.reset_index(drop=True, inplace=True)
with temp_to_rename(cache_path) as temp_path:
df.to_feather(temp_path)
df.set_index(["FIPS", "Date"],
drop=True,
inplace=True,
verify_integrity=True)
df.sort_index(inplace=True)
return df
def get_world(session, args):
"""Returns data organized into a tree rooted at a World region.
Warnings are captured and printed, then raise a ValueError exception."""
cache_path = cache_policy.cached_path(session, _world_cache_key(args))
if cache_path.exists():
logging.info(f"Loading cached world: {cache_path}")
with cache_path.open(mode="rb") as cache_file:
return pickle.load(cache_file)
with collecting_warnings(allow_regex=KNOWN_WARNINGS_REGEX) as warnings:
world = _compute_world(session, args)
if warnings:
raise ValueError(f"{len(warnings)} warnings found combining data")
logging.info(f"Saving cached world: {cache_path}")
with cache_policy.temp_to_rename(cache_path, mode="wb") as cache_file:
pickle.dump(world, cache_file)
return world
def get_covid(session):
"""Returns a DataFrame of COVID-19 daily records."""
cache_path = cached_path(session, f"{REPORTS_URL}:feather")
if cache_path.exists():
df = pandas.read_feather(cache_path)
else:
reports = []
start_day = pandas.Timestamp("01-22-2020", tz="UTC")
days = pandas.date_range(start_day, pandas.Timestamp.now(tz="UTC"))
for day in days:
mm_dd_yyyy = day.strftime("%m-%d-%Y")
response = session.get(f"{REPORTS_URL}/{mm_dd_yyyy}.csv")
if response.status_code != 404:
response.raise_for_status()
data = io.StringIO(response.text)
rep = pandas.read_csv(data, na_values="", keep_default_na=False)
rep.drop(
columns=[
"Active",
"Case-Fatality_Ratio",
"Case_Fatality_Ratio",
"Combined_Key",
"FIPS",
"Incident_Rate",
"Incidence_Rate",
"Lat",
"Long_",
"Latitude",
"Longitude",
"Recovered",
],
inplace=True,
errors="ignore",
)
rep.rename(
columns={
"Country/Region": "Country_Region",
"Last Update": "Last_Update",
"Province/State": "Province_State",
},
inplace=True,
)
rep["Date"] = day
rep.Last_Update = pandas.to_datetime(rep.Last_Update, utc=True)
reports.append(rep)
place_cols = ["Country_Region", "Province_State", "Admin2"]
ids = get_places(session).reset_index().set_index(place_cols)[["ID"]]
df = pandas.concat(reports, ignore_index=True)
for str_col in df.select_dtypes("object").columns:
df[str_col].fillna("", inplace=True)
df = df.merge(ids, left_on=place_cols, right_index=True)
df.drop(columns=place_cols, inplace=True)
df.reset_index(drop=True, inplace=True)
with temp_to_rename(cache_path) as temp_path:
df.to_feather(temp_path)
df = df.groupby(["ID", "Date"], as_index=False).first()
df.set_index(["ID", "Date"], inplace=True, verify_integrity=True)
return df
import argparse
from covid import cache_policy
from covid import logging_policy # noqa
parser = argparse.ArgumentParser(parents=[cache_policy.argument_parser])
parser.add_argument("--id", type=int)
args = parser.parse_args()
session = cache_policy.new_session(args)
print("Loading places...")
places = get_places(session)
places.info()
print()
print("Cache:", cached_path(session, f"{REPORTS_URL}:feather"))
print("Loading COVID data...")
covid = get_covid(session)
covid.info()
print()
if args.id:
print(f"=== PLACE ID={args.id} ===")
place = places.loc[args.id]
print(place)
print()
print(f"=== COVID ID={args.id} ===")
print(covid.loc[args.id])
else:
def credits():
return {"https://healthdata.gov/": "HealthData.gov"}
if __name__ == "__main__":
import argparse
from covid import cache_policy
from covid import logging_policy # noqa
parser = argparse.ArgumentParser(parents=[cache_policy.argument_parser])
args = parser.parse_args()
session = cache_policy.new_session(args)
print("Cache:", cached_path(session, f"{DATA_URL}:feather"))
print("Loading hospitalizations...")
df = get_hospitalizations(session)
df.info(verbose=True, show_counts=True)
print()
print("=== FACILITIES ===")
key_cols = ["fips_code", "hospital_pk"]
for (fips, pk), rows in df.groupby(level=key_cols):
last = rows.iloc[-1]
print(f"--- fips={fips} pk={pk} t={last.name[-1]} ---")
for key, val in last.iteritems():
print(key)
print(f" {val}")
print()