def predict_cases_sldata():
Df_dataset = Datasets.SLDataPreprocess()
new1 = Df_dataset[["Date_Added", "Detected_Prefecture"]]
a = new1.groupby("Date_Added").size().values
df1 = new1.drop_duplicates(subset="Date_Added").assign(Count=a)
dfnew = df1.pivot_table('Count', ['Date_Added'], 'Detected_Prefecture')
dfnew.fillna(0, inplace=True)
return dfnew
fig0 = px.line(dfsumbyday, x='Date', y='Confirmed', title="<b>Total Cases</b>")
fig0.update_layout(template='plotly_dark')
dfsumbyday = pd.read_csv('sumbyday.csv')
dfsumbyday['Date'] = dfsumbyday['Date'].astype('datetime64')
dfsumbyday['Date'] = dfsumbyday['Date'].astype('datetime64')
fig1 = px.line(dfsumbyday,
x='Date',
y='Recovered',
title="<b>Total Recovered</b>")
fig1.update_layout(template='plotly_dark')
df = Datasets.SLDataPreprocess()
df['Date_Announced'] = df['Date_Announced'].astype('datetime64')
df['Date_Added'] = df['Date_Added'].astype('datetime64')
newDataSet = df.groupby([
'Detected_Prefecture'
])['Number'].count().to_frame().rename(columns={
'Detected_Prefecture': 'Cases'
}).reset_index()
fig2 = px.bar(newDataSet,
x='Detected_Prefecture',
y='Number',
title="<b>Cases By City</b>",
height=600)
# Add figure title
fig2.update_layout(template='plotly_dark')
