def main():
st.title("Data")
date = datetime.today()
df = None
load_state = st.text('Loading data......')
while True:
try:
df = load_data(fetch_url(date))
except Exception as e:
date = date - timedelta(days=1)
continue
break
load_state.text("Loading data......done!")
if st.checkbox("Show raw data"):
st.subheader('Raw data')
st.write(df)
st.subheader("The numbers so far")
st.markdown("* There are **{}** countries that have been affected by the COVID-19 virus.".format(len(df["Country_Region"].unique())))
st.markdown("* The virus has affected **{:.2f}M** people and caused the death of **{:.2f}K**.".format(df["Confirmed"].sum()/1000000,
df["Deaths"].sum()/1000))
h_confirmed = df.groupby("Country_Region").agg({"Confirmed": "sum"}).nlargest(1,"Confirmed")
st.markdown("* **{}** has the largest number of confirmed cases with **{:.2f}M** confirmed cases.".format(h_confirmed.index.values[0],
h_confirmed["Confirmed"].values[0]/1000000))
h_deaths = df.groupby("Country_Region").agg({"Deaths": "sum"}).nlargest(1,"Deaths")
st.markdown("* **{}** has the largest number of deaths with **{:.2f}K** deaths.".format(h_deaths.index.values[0],
h_deaths["Deaths"].values[0]/1000))
h_recovered = df.groupby("Country_Region").agg({"Recovered": "sum"}).nlargest(1,"Recovered")
st.markdown("* **{}** has the largest number of recoveries with **{:.2f}M** recovered.".format(h_recovered.index.values[0],
h_recovered["Recovered"].values[0]/1000000))
def main():
pio.templates.default = "plotly_dark"
date = datetime.today()
df = None
while True:
try:
df = load_data(fetch_url(date))
except Exception as e:
date = date - timedelta(days=1)
continue
break
time_series_dict = load_time_series()
granularity = st.sidebar.selectbox("Granularity", ["Worldwide", "Country"])
if granularity == "Country":
country = st.sidebar.selectbox("country",
df["Country_Region"].unique())
st.title(country)
graph_type = st.selectbox(
"Choose visualization",
["Total Count", "Timeline", "Province/State"])
if graph_type == "Total Count":
st.subheader("One day change")
load_day_change(time_series_dict,
time_series_dict.keys(),
granularity,
country=country)
fig = plot_snapshot_numbers(df, px.colors.qualitative.D3,
date.date(), country)
st.plotly_chart(fig)
elif graph_type == "Timeline":
feature = st.selectbox("Select one",
["Confirmed", "Deaths", "Recovered"])
fig, _ = plot_timeline(time_series_dict[feature],
feature,
country=country)
st.plotly_chart(fig)
elif graph_type == "Province/State":
fig = plot_province(df, country)
if fig is not None:
fig_drilled = None
flag = st.checkbox("Summary (click and scroll)")
st.subheader("Hover Map")
st.plotly_chart(fig)
if flag:
if country == "US":
fig_drilled = plot_province_drilled(
load_data(fetch_url(date, country="US")), country)
else:
fig_drilled = plot_province_drilled(df, country)
if fig_drilled is not None:
st.subheader("Summary")
st.plotly_chart(fig_drilled)
else:
# TODO(Sayar): Add values for deltas
st.title("Worldwide")
st.write("\n")
st.info("Data updated as on {}".format(
date.date().strftime("%d %B, %Y")))
graph_type = st.sidebar.selectbox(
"Choose visualization",
["Total Count", "Top affected/recovered", "Timeline"])
if graph_type == "Total Count":
st.subheader("One day change")
load_day_change(time_series_dict, time_series_dict.keys(),
granularity)
fig = plot_snapshot_numbers(df, px.colors.qualitative.D3,
date.date())
st.plotly_chart(fig)
elif graph_type == "Top affected/recovered":
fig = plot_top_countries(df, px.colors.qualitative.D3, date.date())
st.plotly_chart(fig)
elif graph_type == "Timeline":
feature = st.selectbox("Select one",
["Confirmed", "Deaths", "Recovered"])
fig, _ = plot_timeline(time_series_dict[feature], feature)
st.plotly_chart(fig)