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title = fill_title_and_subtitle("The Race Against Covid", "Visualizing Cumulative Statistics of Disease vs Vaccine")

fig = go.Figure(data=[
                go.Scatter(
                    mode="lines+markers",
                    name="Total Deaths",
                    x=data['date'], 
                    y=data['cumulative_total_deaths'],
                    marker_color="crimson",
                ),

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title = fill_title_and_subtitle("Vaccine vs Virus", "Comparing the total number of daily new cases and daily vaccinations globally")
fig = go.Figure(data=[
                go.Bar(
                    name="New Cases",
                    x=data['date'], 
                    y=data['daily_new_cases'],
                    marker_color="crimson",
                ),
                go.Bar(
                    name="Vaccinated",

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countries = short.groupby('country')['total_vaccinations'].max().sort_values(ascending=False)[:10].index.tolist()

title = get_multi_line_title("Vaccination Progress", "Rate of vaccinations for the top-10 vaccinated countries")

line_plots = []
for c in countries:
    vacc_data = short[short.country == c]
    line_plots.append(
        go.Scatter(
            name = c,

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from datetime import datetime
vaccs = vaccine_df.copy()
daily = daily_df.copy()
daily.head()
# standardise the dates 
vaccs.date =vaccs.date.apply(lambda x: datetime.strptime(x, "%Y-%m-%d"))
daily.date =daily.date.apply(lambda x: datetime.strptime(x, "%Y-%m-%d"))

# use only common countries and dates 
countries = vaccs.dropna(subset=['daily_vaccinations'])['country'].unique()

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import math
# unique dates 
dates = vaccine_df.date.unique().tolist()
#For more smoother animations
dates.extend(['2020-12-12', '2020-12-13']) 

# unique countries 
countries = vaccine_df.country.unique().tolist()

# for easy processing 

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title = ("Death Rates", "Percentage of the confired cases have died from Covid-19")
data = summary_df.copy()
data['death_rate'] =  (data['total_deaths']/data['total_confirmed'])*100
data = data.dropna(subset=['death_rate'])
fig = px.scatter_geo(data, locations="country", color="continent",
locationmode='country names',
hover_name="country", size="death_rate",
projection="natural earth")
fig.update_layout(title=title, title_x=0.45)
fig.show()

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def fill_title_and_subtitle(title:str, subtitle:str):
    return f"{title}<br><sub>{subtitle}</sub>"

def create_bar_chart(data: pd.DataFrame, xcolumn: str, ycolumn:str, title:str, colors:str, ylabel="Count", n=None):
    hovertemplate ='<br><b>%{x}</b>'+f'<br><b>{ylabel}: </b>'+'%{y}<br><extra></extra>'    
    data = data.sort_values(ycolumn, ascending=False).dropna(subset=[ycolumn])        
    
    if n is not None: 
        data = data.iloc[:n]
    else:

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#We will create an aggregated dataset by aggreagting the required columns using previous function.
summary_cols = ['people_vaccinated', 
                     'people_vaccinated_per_hundred', 
                     'people_fully_vaccinated', 
                     'people_fully_vaccinated_per_hundred', 
                     'total_vaccinations_per_hundred', 
                     'total_vaccinations']

summary = summary_df.set_index("country")
vaccines = vaccine_df[['country', 'vaccines']].drop_duplicates().set_index('country')

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vaccine_df.country = vaccine_df.country.replace().replace({
    "Czechia": "Czech Republic", 
    "United States": "USA", 
    "United Kingdom": "UK", 
    "Isle of Man": "Isle Of Man",
    "Republic of Ireland": "Ireland",
    "Northern Cyprus" : "Cyprus"
})

#These 4 countries are a part of UK

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def aggregate(df: pd.Series, agg_col: str) -> pd.DataFrame:
    
    data = df.groupby("country")[agg_col].max()
    data = pd.DataFrame(data)
    
    return data