no_negs[no_negs < 0] = 0
rolling = no_negs.rolling(7).mean()
rolling.to_csv("rolling.csv")
lga_movement = []
for col in totals.index:
print(col)
row = {}
row['lga'] = col
row['change'] = rolling.loc[lga_daily.index[-1], col] - rolling.loc[one_week_ago, col]
row['this_week'] = lga_daily[one_week_ago:][col].sum()
row['last_week'] = lga_daily[two_weeks_ago:one_week_ago - timedelta(days=1)][col].sum()
row['weekly_change'] = row['this_week'] - row['last_week']
row['date'] = lga_daily.index[-1].strftime('%Y-%m-%d')
lga_movement.append(row)
lga_df_movement1 = pd.DataFrame(lga_movement)
#%%
syncData(lga_movement,'2020/07/vic-corona-map{test}'.format(test=test), "vicChange-2")
syncData(totals_30day.to_dict(orient='records'),'2020/07/vic-corona-map{test}'.format(test=test), "vicTotals")
# short['rolling_mean'] = df.groupby('lga')['cases'].transform(lambda x: x.rolling(7, 1).mean())
negative_countries_deaths = []
for country in deaths_countries:
temp_series = deaths_new[deaths_new[country] < 0]
if len(temp_series.index) > 0:
negative_countries_deaths.append(country)
print(",".join(negative_countries_deaths))
deaths_new[deaths_new < 0] = 0
#%%
confirmedDailyData = json.dumps(ecdc_new.reset_index().to_dict('records'))
syncData(confirmedDailyData, "2020/03/coronavirus-widget-data",
"confirmed_daily_ecdc.json")
confirmedTotalData = json.dumps(ecdc_high.reset_index().to_dict('records'))
syncData(confirmedTotalData, "2020/03/coronavirus-widget-data",
"confirmed_total_ecdc.json")
negativeCaseData = json.dumps(negative_cases)
syncData(negativeCaseData, "2020/03/coronavirus-widget-data",
"negative_case_countries.json")
#%%
confirmedDailyDeathData = json.dumps(
deaths_new.reset_index().to_dict('records'))
syncData(confirmedDailyDeathData, "2020/03/coronavirus-widget-data",
"confirmed_daily_deaths_ecdc.json")
with open('latest.json') as json_file:
latestJson = json.load(json_file)
latestObj = []
shortlist = ["Australia", "United Kingdom", "US"]
for row in latestJson['features']:
latestObj.append(row['attributes'])
latest = pd.DataFrame(latestObj)
latest_country = latest.groupby(["Country_Region"]).sum()
latest_country.loc['Total'] = latest_country.sum()
latestData = json.dumps(latest_country.reset_index().to_dict('records'))
syncData(latestData, "2020/03/coronavirus-widget-data",
"latest{preview}.json".format(preview=preview))
# For confirmed cases, since we want it for charts
confirmed = pd.read_csv("time_series_covid19_confirmed_global.csv")
shortlist = ["United Kingdom", "US", "Total"]
confirmed.loc['Total'] = confirmed.sum(numeric_only=True, axis=0)
confirmed.loc[['Total'], ["Country/Region"]] = "Total"
confirmed_country = confirmed.groupby(["Country/Region"]).sum()
over100 = confirmed_country[confirmed_country.iloc[:, -1] > 100]
data['notes'] = notes
#handle weird cases
if data['entry'] != "":
if data['notes'] == "German literary movement":
data['entry'] = "Sturm und Drang"
#print data
styleData.append(data)
final = {"data": styleData, "lastUpdated": currDate + " " + currTime}
if not errors:
syncData(json.dumps(final, indent=4), "australia/2014/styleguide",
"style-guide.json")
with open('style-guide.json', 'w') as f:
json.dump(final, f, indent=4)
# output = StringIO.StringIO()
# output.write('{"data":')
# output.write(json.dumps(styleData))
# output.write(', "lastUpdated":"' + currDate + ' ' + currTime + '"')
# output.write('}')
# print "Connecting to S3"
# bucket = conn.get_bucket('gdn-cdn')
# from boto.s3.key import Key
headers = {
'User-Agent':
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'
}
r = requests.get('https://covidlive.com.au/covid-live.json', headers=headers)
#%%
data = r.json()
df = pd.read_json(r.text)
print(list(df.columns.values))
cols = [
'REPORT_DATE', 'LAST_UPDATED_DATE', 'ACTIVE_CNT', 'PREV_ACTIVE_CNT',
'CODE', 'NAME', 'RECOV_CNT', 'VACC_DIST_CNT', 'PREV_VACC_DIST_CNT',
'VACC_DOSE_CNT', 'PREV_VACC_DOSE_CNT', 'VACC_PEOPLE_CNT',
'PREV_VACC_PEOPLE_CNT'
]
df_aus = df[df['NAME'] == "Australia"]
df_aus = df_aus[df_aus['REPORT_DATE'] >= '2021-02-14']
final = df_aus[cols]
finalJson = final.to_json(orient='records')
#%%
syncData(finalJson, "2021/02/coronavirus-widget-data", "aus-vaccines.json")
no_negs = lga_daily
no_negs[no_negs < 0] = 0
rolling = no_negs.rolling(7).mean()
rolling.to_csv("rolling.csv")
lga_movement = []
for col in totals.index:
print(col)
row = {}
row['lga'] = col
row['change'] = rolling.loc[lga_daily.index[-1], col] - rolling.loc[one_week_ago, col]
row['this_week'] = lga_daily[one_week_ago:][col].sum()
row['last_week'] = lga_daily[two_weeks_ago:one_week_ago - timedelta(days=1)][col].sum()
row['weekly_change'] = row['this_week'] - row['last_week']
row['date'] = lga_daily.index[-1].strftime('%Y-%m-%d')
lga_movement.append(row)
lga_df_movement1 = pd.DataFrame(lga_movement)
#%%
syncData(lga_movement,'2020/07/vic-corona-map{test}'.format(test=test), "vicChange-2")
# short['rolling_mean'] = df.groupby('lga')['cases'].transform(lambda x: x.rolling(7, 1).mean())