def get_state_level(d: dict) -> dict:
idx = [
i
for i in range(len(d["Province/State"]))
if d["Province/State"][i] in states.values()
]
return {k: np.array(v)[idx] for k, v in d.items()}
def _read_nytimes_data(url: str) -> (dict, dict):
response = urlopen(url)
byts = response.read()
data = io.StringIO(byts.decode())
ori_data = pd.read_csv(data).drop('fips', axis=1)
dates = ori_data.date.unique()
cases = {}
deaths = {}
for name in states.values():
df_filter = ori_data['state'] == name
filter_data = ori_data[df_filter]
cases.setdefault('state', []).append(name)
deaths.setdefault('state', []).append(name)
for date in dates:
if date in filter_data.date.unique():
cases.setdefault(date, []).append(
filter_data[filter_data.date == date].iloc[0]['cases'])
deaths.setdefault(date, []).append(
filter_data[filter_data.date == date].iloc[0]['deaths'])
else:
cases.setdefault(date, []).append(0)
deaths.setdefault(date, []).append(0)
return (cases, deaths)
else:
try:
out[k] = list(map(int, data[k]))
except ValueError:
out[k] = [0]
return out
def get_nytimes_state_level(d: dict) -> dict:
idx = [i for i in range(len(d["state"]))]
return {k: np.array(v)[idx] for k, v in d.items()}
datafile = "https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"
state_data = {}
for n in states.values():
data = []
(cases_dict, deaths_dict) = _read_nytimes_data(datafile)
cases_df = pd.DataFrame.from_dict(
get_nytimes_state_level(_convert_nytimes_data(cases_dict)))
deaths_df = pd.DataFrame.from_dict(
get_nytimes_state_level(_convert_nytimes_data(deaths_dict)))
case_df_filter = cases_df['state'] == n
death_df_filter = deaths_df['state'] == n
out = {
'confirmed cases': cases_df[case_df_filter].drop('state', axis=1),
'deaths': deaths_df[death_df_filter].drop('state', axis=1)
}
# Generate all the traces.
# Each distancing rate is a different plot, which is made visible with the update buttons
for key, df_values in out.items():
